
import numpy as np
import pandas as pd

data_url = "http://lib.stat.cmu.edu/datasets/boston"
raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
target = raw_df.values[1::2, 2]

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y)


from sklearn.linear_model import LinearRegression
linreg = LinearRegression().fit(X_train,y_train)


m=len(X)
print('Erreur de test: ',(1/m)*np.sum((y_test-linreg.predict(X_test))**2))

Erreur de test:  5.652671166106032


print('Score R²: ',linreg.score(X_test,y_test))

Score R²:  0.6832195864696119


from sklearn.linear_model import LassoCV
lasso = LassoCV(alphas=np.logspace(-10,10,40),cv=5)
lasso.fit(X_train,y_train)
print('Paramètre alpha sélectionné:',lasso.alpha_)
print('R² Score: ',lasso.score(X_test,y_test))

Paramètre alpha sélectionné: 0.00151177507061566
R² Score:  0.6827297829114514


from sklearn.linear_model import RidgeCV
ridge = RidgeCV(cv=5,alphas=np.logspace(-10,10,40))
ridge.fit(X_train,y_train)
print('Paramètre alpha sélectionné:',ridge.alpha_)
print('Score R²: ',ridge.score(X_test,y_test))

Paramètre alpha sélectionné: 0.17012542798525856
Score R²:  0.6806291614609656


from sklearn.kernel_ridge import KernelRidge
kr = KernelRidge(kernel='rbf',alpha=.001,gamma=.001)
kr.fit(X_train,y_train)

print('Score R²: ',kr.score(X_test,y_test))

Score R²:  0.39692745632127113


from sklearn.model_selection import GridSearchCV


kr = GridSearchCV(KernelRidge(kernel='rbf'), cv=5,
                  param_grid={"alpha": np.logspace(-15,-10,10)
                              ,"gamma": np.logspace(-11,-7,10)},verbose=3)
kr.fit(X_train,y_train)

Fitting 5 folds for each of 100 candidates, totalling 500 fits
[CV] alpha=1e-15, gamma=1e-11 ........................................
[CV] ............ alpha=1e-15, gamma=1e-11, score=0.723, total=   0.0s
[CV] alpha=1e-15, gamma=1e-11 ........................................
[CV] ............ alpha=1e-15, gamma=1e-11, score=0.636, total=   0.0s
[CV] alpha=1e-15, gamma=1e-11 ........................................
[CV] ............ alpha=1e-15, gamma=1e-11, score=0.821, total=   0.0s
[CV] alpha=1e-15, gamma=1e-11 ........................................
[CV] ............ alpha=1e-15, gamma=1e-11, score=0.749, total=   0.0s
[CV] alpha=1e-15, gamma=1e-11 ........................................
[CV] ............ alpha=1e-15, gamma=1e-11, score=0.696, total=   0.0s
[CV] alpha=1e-15, gamma=2.7825594022071258e-11 .......................
[CV]  alpha=1e-15, gamma=2.7825594022071258e-11, score=0.364, total=   0.0s
[CV] alpha=1e-15, gamma=2.7825594022071258e-11 .......................
[CV]  alpha=1e-15, gamma=2.7825594022071258e-11, score=0.601, total=   0.0s
[CV] alpha=1e-15, gamma=2.7825594022071258e-11 .......................
[CV]  alpha=1e-15, gamma=2.7825594022071258e-11, score=0.839, total=   0.0s
[CV] alpha=1e-15, gamma=2.7825594022071258e-11 .......................
[CV]  alpha=1e-15, gamma=2.7825594022071258e-11, score=0.715, total=   0.0s
[CV] alpha=1e-15, gamma=2.7825594022071258e-11 .......................
[CV]  alpha=1e-15, gamma=2.7825594022071258e-11, score=-1.900, total=   0.0s
[CV] alpha=1e-15, gamma=7.742636826811278e-11 ........................
[CV]  alpha=1e-15, gamma=7.742636826811278e-11, score=-0.205, total=   0.0s
[CV] alpha=1e-15, gamma=7.742636826811278e-11 ........................
[CV]  alpha=1e-15, gamma=7.742636826811278e-11, score=0.488, total=   0.0s
[CV] alpha=1e-15, gamma=7.742636826811278e-11 ........................
[CV]  alpha=1e-15, gamma=7.742636826811278e-11, score=0.333, total=   0.0s
[CV] alpha=1e-15, gamma=7.742636826811278e-11 ........................
[CV]  alpha=1e-15, gamma=7.742636826811278e-11, score=-0.813, total=   0.0s
[CV] alpha=1e-15, gamma=7.742636826811278e-11 ........................
[CV]  alpha=1e-15, gamma=7.742636826811278e-11, score=-0.333, total=   0.0s
[CV] alpha=1e-15, gamma=2.1544346900318867e-10 .......................
[CV]  alpha=1e-15, gamma=2.1544346900318867e-10, score=0.685, total=   0.0s
[CV] alpha=1e-15, gamma=2.1544346900318867e-10 .......................
[CV]  alpha=1e-15, gamma=2.1544346900318867e-10, score=0.735, total=   0.0s
[CV] alpha=1e-15, gamma=2.1544346900318867e-10 .......................

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "

[CV]  alpha=1e-15, gamma=2.1544346900318867e-10, score=0.851, total=   0.0s
[CV] alpha=1e-15, gamma=2.1544346900318867e-10 .......................
[CV]  alpha=1e-15, gamma=2.1544346900318867e-10, score=0.816, total=   0.0s
[CV] alpha=1e-15, gamma=2.1544346900318867e-10 .......................
[CV]  alpha=1e-15, gamma=2.1544346900318867e-10, score=0.684, total=   0.0s
[CV] alpha=1e-15, gamma=5.994842503189421e-10 ........................
[CV]  alpha=1e-15, gamma=5.994842503189421e-10, score=0.658, total=   0.0s
[CV] alpha=1e-15, gamma=5.994842503189421e-10 ........................
[CV]  alpha=1e-15, gamma=5.994842503189421e-10, score=0.133, total=   0.0s
[CV] alpha=1e-15, gamma=5.994842503189421e-10 ........................
[CV]  alpha=1e-15, gamma=5.994842503189421e-10, score=0.108, total=   0.0s
[CV] alpha=1e-15, gamma=5.994842503189421e-10 ........................
[CV]  alpha=1e-15, gamma=5.994842503189421e-10, score=0.254, total=   0.0s
[CV] alpha=1e-15, gamma=5.994842503189421e-10 ........................
[CV]  alpha=1e-15, gamma=5.994842503189421e-10, score=-0.882, total=   0.0s
[CV] alpha=1e-15, gamma=1.6681005372000556e-09 .......................
[CV]  alpha=1e-15, gamma=1.6681005372000556e-09, score=0.753, total=   0.0s
[CV] alpha=1e-15, gamma=1.6681005372000556e-09 .......................
[CV]  alpha=1e-15, gamma=1.6681005372000556e-09, score=0.311, total=   0.0s
[CV] alpha=1e-15, gamma=1.6681005372000556e-09 .......................
[CV]  alpha=1e-15, gamma=1.6681005372000556e-09, score=0.502, total=   0.0s
[CV] alpha=1e-15, gamma=1.6681005372000556e-09 .......................
[CV]  alpha=1e-15, gamma=1.6681005372000556e-09, score=0.615, total=   0.0s
[CV] alpha=1e-15, gamma=1.6681005372000556e-09 .......................
[CV]  alpha=1e-15, gamma=1.6681005372000556e-09, score=-1.692, total=   0.0s
[CV] alpha=1e-15, gamma=4.641588833612773e-09 ........................
[CV]  alpha=1e-15, gamma=4.641588833612773e-09, score=0.823, total=   0.0s
[CV] alpha=1e-15, gamma=4.641588833612773e-09 ........................
[CV]  alpha=1e-15, gamma=4.641588833612773e-09, score=0.706, total=   0.0s
[CV] alpha=1e-15, gamma=4.641588833612773e-09 ........................
[CV]  alpha=1e-15, gamma=4.641588833612773e-09, score=0.787, total=   0.0s
[CV] alpha=1e-15, gamma=4.641588833612773e-09 ........................

/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "

[CV]  alpha=1e-15, gamma=4.641588833612773e-09, score=0.489, total=   0.0s
[CV] alpha=1e-15, gamma=4.641588833612773e-09 ........................
[CV]  alpha=1e-15, gamma=4.641588833612773e-09, score=-0.061, total=   0.0s
[CV] alpha=1e-15, gamma=1.2915496650148827e-08 .......................
[CV]  alpha=1e-15, gamma=1.2915496650148827e-08, score=0.385, total=   0.0s
[CV] alpha=1e-15, gamma=1.2915496650148827e-08 .......................
[CV]  alpha=1e-15, gamma=1.2915496650148827e-08, score=0.650, total=   0.0s
[CV] alpha=1e-15, gamma=1.2915496650148827e-08 .......................
[CV]  alpha=1e-15, gamma=1.2915496650148827e-08, score=0.199, total=   0.0s
[CV] alpha=1e-15, gamma=1.2915496650148827e-08 .......................
[CV]  alpha=1e-15, gamma=1.2915496650148827e-08, score=0.682, total=   0.0s
[CV] alpha=1e-15, gamma=1.2915496650148827e-08 .......................
[CV]  alpha=1e-15, gamma=1.2915496650148827e-08, score=0.722, total=   0.0s
[CV] alpha=1e-15, gamma=3.593813663804626e-08 ........................
[CV]  alpha=1e-15, gamma=3.593813663804626e-08, score=-0.602, total=   0.0s
[CV] alpha=1e-15, gamma=3.593813663804626e-08 ........................
[CV]  alpha=1e-15, gamma=3.593813663804626e-08, score=0.308, total=   0.0s
[CV] alpha=1e-15, gamma=3.593813663804626e-08 ........................
[CV]  alpha=1e-15, gamma=3.593813663804626e-08, score=0.686, total=   0.0s
[CV] alpha=1e-15, gamma=3.593813663804626e-08 ........................
[CV]  alpha=1e-15, gamma=3.593813663804626e-08, score=0.591, total=   0.0s
[CV] alpha=1e-15, gamma=3.593813663804626e-08 ........................
[CV]  alpha=1e-15, gamma=3.593813663804626e-08, score=-0.864, total=   0.0s
[CV] alpha=1e-15, gamma=1e-07 ........................................
[CV] ........... alpha=1e-15, gamma=1e-07, score=-5.556, total=   0.0s
[CV] alpha=1e-15, gamma=1e-07 ........................................

/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "

[CV] ............ alpha=1e-15, gamma=1e-07, score=0.216, total=   0.0s
[CV] alpha=1e-15, gamma=1e-07 ........................................
[CV] ........... alpha=1e-15, gamma=1e-07, score=-6.500, total=   0.0s
[CV] alpha=1e-15, gamma=1e-07 ........................................
[CV] ............ alpha=1e-15, gamma=1e-07, score=0.655, total=   0.0s
[CV] alpha=1e-15, gamma=1e-07 ........................................
[CV] .......... alpha=1e-15, gamma=1e-07, score=-17.875, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=1e-11 ........................
[CV]  alpha=3.593813663804626e-15, gamma=1e-11, score=0.723, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=1e-11 ........................
[CV]  alpha=3.593813663804626e-15, gamma=1e-11, score=0.636, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=1e-11 ........................
[CV]  alpha=3.593813663804626e-15, gamma=1e-11, score=0.822, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=1e-11 ........................
[CV]  alpha=3.593813663804626e-15, gamma=1e-11, score=0.758, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=1e-11 ........................
[CV]  alpha=3.593813663804626e-15, gamma=1e-11, score=-0.638, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11, score=0.291, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11, score=0.573, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11, score=0.732, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11, score=0.768, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11 .......

/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "

[CV]  alpha=3.593813663804626e-15, gamma=2.7825594022071258e-11, score=0.168, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=7.742636826811278e-11 ........
[CV]  alpha=3.593813663804626e-15, gamma=7.742636826811278e-11, score=0.631, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=7.742636826811278e-11 ........
[CV]  alpha=3.593813663804626e-15, gamma=7.742636826811278e-11, score=0.501, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=7.742636826811278e-11 ........
[CV]  alpha=3.593813663804626e-15, gamma=7.742636826811278e-11, score=0.106, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=7.742636826811278e-11 ........
[CV]  alpha=3.593813663804626e-15, gamma=7.742636826811278e-11, score=0.691, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=7.742636826811278e-11 ........
[CV]  alpha=3.593813663804626e-15, gamma=7.742636826811278e-11, score=-1.039, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10, score=-1.739, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10, score=0.590, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10, score=0.855, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10, score=0.753, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10 .......
[CV]  alpha=3.593813663804626e-15, gamma=2.1544346900318867e-10, score=0.675, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=5.994842503189421e-10 ........
[CV]  alpha=3.593813663804626e-15, gamma=5.994842503189421e-10, score=0.577, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=5.994842503189421e-10 ........
[CV]  alpha=3.593813663804626e-15, gamma=5.994842503189421e-10, score=0.301, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=5.994842503189421e-10 ........
[CV]  alpha=3.593813663804626e-15, gamma=5.994842503189421e-10, score=-0.705, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=5.994842503189421e-10 ........
[CV]  alpha=3.593813663804626e-15, gamma=5.994842503189421e-10, score=-0.632, total=   0.0s
[CV] alpha=3.593813663804626e-15, gamma=5.994842503189421e-10 ........

/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:190: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.
  warnings.warn("Singular matrix in solving dual problem. Using "


print('Paramètres sélectionnés:',kr.best_params_)
print('Score:',kr.score(X_test,y_test))

Paramètres sélectionnés: {'alpha': 2.7825594022071258e-11, 'gamma': 1.2915496650148827e-08}
Score: 0.8427016651966951


krpoly = GridSearchCV(KernelRidge(kernel='poly',coef0=1), cv=5,
                  param_grid={'degree': [10,11,12]
                              ,"alpha": np.logspace(-13,-11,5)
                              ,"gamma": np.logspace(-11,-9,5)},verbose=3)
krpoly.fit(X_train,y_train)

Fitting 5 folds for each of 75 candidates, totalling 375 fits
[CV] alpha=1e-13, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-11, score=0.768, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-11, score=0.665, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-11, score=0.860, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-11, score=0.806, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-11, score=0.711, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683794e-11, score=0.822, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683794e-11, score=0.729, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683794e-11, score=0.881, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683794e-11, score=0.853, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683794e-11, score=0.807, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-10, score=0.785, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-10, score=0.783, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-10, score=0.865, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-10, score=0.874, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-10, score=0.855, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683795e-10, score=0.822, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683795e-10, score=0.796, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683795e-10, score=0.827, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683795e-10, score=0.872, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=10, gamma=3.1622776601683795e-10, score=0.871, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-09, score=0.829, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-09, score=0.749, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-09, score=0.812, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-09, score=0.846, total=   0.0s
[CV] alpha=1e-13, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=10, gamma=1e-09, score=0.744, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-11, score=0.774, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-11, score=0.675, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-11, score=0.858, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-11 .............................

[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.48987e-17): result may not be accurate.
  overwrite_a=False)
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.13783e-17): result may not be accurate.
  overwrite_a=False)
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=8.60536e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.90685e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=8.37301e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.21976e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.47005e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.00959e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.81067e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.02862e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.00157e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.39872e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=5.70716e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.45341e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.54706e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=5.57561e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=8.26328e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.82676e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.73979e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=5.83002e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.33031e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.11606e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.45115e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.24005e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.04559e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=8.31883e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.98039e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=8.1768e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.38004e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.95234e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.18771e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.33424e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.85069e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=8.49667e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.85036e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.55811e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.34134e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=5.59598e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.65806e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.02781e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.14266e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.30413e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.45558e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=5.56172e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.5371e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.23597e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.38228e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.34931e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.99656e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.27744e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.22725e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.60968e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.77222e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.36523e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.46974e-17): result may not be accurate.
  overwrite_a=False)

[CV] . alpha=1e-13, degree=11, gamma=1e-11, score=0.812, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-11, score=0.725, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683794e-11, score=0.818, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683794e-11, score=0.735, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683794e-11, score=0.881, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683794e-11, score=0.853, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683794e-11, score=0.817, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-10, score=0.794, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-10, score=0.781, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-10, score=0.863, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-10, score=0.872, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-10, score=0.855, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683795e-10, score=0.809, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683795e-10, score=0.797, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683795e-10, score=0.832, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683795e-10, score=0.871, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=11, gamma=3.1622776601683795e-10, score=0.866, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-09, score=0.844, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-09, score=0.745, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-09, score=0.810, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-09, score=0.837, total=   0.0s
[CV] alpha=1e-13, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=11, gamma=1e-09, score=0.740, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-11, score=0.782, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-11, score=0.674, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-11, score=0.862, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-11, score=0.807, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-11, score=0.733, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683794e-11 ............

/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.69934e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.64371e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.96325e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.0426e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.83503e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.65131e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.25107e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=5.50285e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.53637e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.13527e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.18751e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.31009e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.51599e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=5.91917e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.05017e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=5.36027e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.41257e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=6.68761e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.00811e-17): result may not be accurate.
  overwrite_a=False)
/home/joon/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py:188: LinAlgWarning: Ill-conditioned matrix (rcond=7.41866e-17): result may not be accurate.
  overwrite_a=False)

[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683794e-11, score=0.818, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683794e-11, score=0.738, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683794e-11, score=0.878, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683794e-11, score=0.852, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683794e-11, score=0.819, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-10, score=0.800, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-10, score=0.788, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-10, score=0.867, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-10, score=0.870, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-10, score=0.857, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683795e-10, score=0.797, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683795e-10, score=0.794, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683795e-10, score=0.823, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683795e-10, score=0.867, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-13, degree=12, gamma=3.1622776601683795e-10, score=0.871, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-09, score=0.843, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-09, score=0.741, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-09, score=0.806, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-09, score=0.826, total=   0.0s
[CV] alpha=1e-13, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-13, degree=12, gamma=1e-09, score=0.699, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-11, score=0.746, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-11, score=0.649, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-11, score=0.841, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-11, score=0.779, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-11, score=0.706, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11, score=0.804, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11, score=0.695, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11, score=0.879, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11, score=0.826, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683794e-11, score=0.770, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-10, score=0.809, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-10, score=0.765, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-10, score=0.883, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-10, score=0.869, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-10, score=0.836, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10, score=0.807, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10, score=0.794, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10, score=0.853, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10, score=0.875, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=3.1622776601683795e-10, score=0.867, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-09, score=0.820, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-09, score=0.778, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-09, score=0.822, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-09, score=0.866, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=10, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=10, gamma=1e-09, score=0.833, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-11, score=0.749, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-11, score=0.653, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-11, score=0.843, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-11, score=0.783, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-11, score=0.704, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11, score=0.810, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11, score=0.699, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11, score=0.880, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11, score=0.830, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683794e-11, score=0.776, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-10, score=0.808, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-10, score=0.771, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-10, score=0.881, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-10, score=0.869, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-10, score=0.842, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10, score=0.807, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10, score=0.795, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10, score=0.848, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10, score=0.877, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=3.1622776601683795e-10, score=0.870, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-09, score=0.820, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-09, score=0.775, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-09, score=0.817, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-09, score=0.862, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=11, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=11, gamma=1e-09, score=0.820, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-11, score=0.751, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-11, score=0.653, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-11, score=0.847, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-11, score=0.787, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-11 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-11, score=0.709, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11, score=0.815, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11, score=0.705, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11, score=0.883, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11, score=0.836, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683794e-11, score=0.783, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-10, score=0.804, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-10, score=0.774, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-10, score=0.880, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-10, score=0.869, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-10 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-10, score=0.844, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10, score=0.811, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10, score=0.794, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10, score=0.845, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10, score=0.876, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=3.1622776601683795e-10, score=0.872, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-09, score=0.829, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-09, score=0.771, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-09, score=0.819, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-09, score=0.859, total=   0.0s
[CV] alpha=3.162277660168379e-13, degree=12, gamma=1e-09 .............
[CV]  alpha=3.162277660168379e-13, degree=12, gamma=1e-09, score=0.803, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-11, score=0.736, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-11, score=0.641, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-11, score=0.830, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-11, score=0.771, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-11, score=0.701, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683794e-11, score=0.768, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683794e-11, score=0.662, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683794e-11, score=0.856, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683794e-11, score=0.799, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683794e-11, score=0.729, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-10, score=0.822, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-10, score=0.734, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-10, score=0.889, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-10, score=0.852, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-10, score=0.811, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683795e-10, score=0.807, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683795e-10, score=0.785, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683795e-10, score=0.870, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683795e-10, score=0.875, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=10, gamma=3.1622776601683795e-10, score=0.857, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-09, score=0.810, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-09, score=0.790, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-09, score=0.833, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-09, score=0.875, total=   0.0s
[CV] alpha=1e-12, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=10, gamma=1e-09, score=0.868, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-11, score=0.737, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-11, score=0.642, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-11, score=0.831, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-11, score=0.772, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-11, score=0.702, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683794e-11, score=0.774, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683794e-11, score=0.667, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683794e-11, score=0.860, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683794e-11, score=0.804, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683794e-11, score=0.735, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-10, score=0.822, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-10, score=0.740, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-10, score=0.890, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-10, score=0.856, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-10, score=0.816, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683795e-10, score=0.807, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683795e-10, score=0.787, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683795e-10, score=0.867, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683795e-10, score=0.876, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=11, gamma=3.1622776601683795e-10, score=0.859, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-09, score=0.811, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-09, score=0.789, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-09, score=0.830, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-09, score=0.874, total=   0.0s
[CV] alpha=1e-12, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=11, gamma=1e-09, score=0.866, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-11, score=0.738, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-11, score=0.643, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-11, score=0.832, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-11, score=0.773, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-11, score=0.701, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683794e-11, score=0.780, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683794e-11, score=0.672, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683794e-11, score=0.864, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683794e-11, score=0.808, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683794e-11, score=0.742, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-10, score=0.820, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-10, score=0.746, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-10, score=0.889, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-10, score=0.858, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-10, score=0.821, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683795e-10, score=0.808, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683795e-10, score=0.789, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683795e-10, score=0.863, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683795e-10, score=0.876, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-12, degree=12, gamma=3.1622776601683795e-10, score=0.861, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-09, score=0.813, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-09, score=0.788, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-09, score=0.828, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-09, score=0.873, total=   0.0s
[CV] alpha=1e-12, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-12, degree=12, gamma=1e-09, score=0.860, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-11, score=0.734, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-11, score=0.638, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-11, score=0.825, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-11, score=0.766, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-11, score=0.700, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11, score=0.744, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11, score=0.647, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11, score=0.839, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11, score=0.780, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683794e-11, score=0.708, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-10, score=0.806, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-10, score=0.695, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-10, score=0.879, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-10, score=0.828, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-10, score=0.771, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10, score=0.815, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10, score=0.766, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10, score=0.884, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10, score=0.869, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=3.1622776601683795e-10, score=0.839, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-09, score=0.809, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-09, score=0.791, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-09, score=0.849, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-09, score=0.877, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=10, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=10, gamma=1e-09, score=0.869, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-11, score=0.734, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-11, score=0.639, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-11, score=0.826, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-11, score=0.767, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-11, score=0.700, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11, score=0.747, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11, score=0.649, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11, score=0.841, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11, score=0.783, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683794e-11, score=0.710, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-10, score=0.811, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-10, score=0.702, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-10, score=0.882, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-10, score=0.833, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-10, score=0.779, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10, score=0.813, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10, score=0.770, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10, score=0.882, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10, score=0.870, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=3.1622776601683795e-10, score=0.843, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-09, score=0.809, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-09, score=0.791, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-09, score=0.846, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-09, score=0.877, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=11, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=11, gamma=1e-09, score=0.870, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-11, score=0.734, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-11, score=0.639, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-11, score=0.827, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-11, score=0.767, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-11 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-11, score=0.700, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11, score=0.750, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11, score=0.651, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11, score=0.843, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11, score=0.785, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683794e-11, score=0.713, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-10, score=0.815, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-10, score=0.708, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-10, score=0.884, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-10, score=0.837, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-10 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-10, score=0.786, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10, score=0.812, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10, score=0.774, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10, score=0.880, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10, score=0.872, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10 
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=3.1622776601683795e-10, score=0.847, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-09, score=0.809, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-09, score=0.792, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-09, score=0.843, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-09, score=0.877, total=   0.0s
[CV] alpha=3.1622776601683794e-12, degree=12, gamma=1e-09 ............
[CV]  alpha=3.1622776601683794e-12, degree=12, gamma=1e-09, score=0.871, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-11, score=0.733, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-11, score=0.636, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-11, score=0.825, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-11, score=0.765, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-11, score=0.701, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683794e-11, score=0.736, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683794e-11, score=0.641, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683794e-11, score=0.830, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683794e-11, score=0.770, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683794e-11, score=0.702, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-10, score=0.769, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-10, score=0.663, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-10, score=0.856, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-10, score=0.800, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-10, score=0.730, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683795e-10, score=0.823, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683795e-10, score=0.734, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683795e-10, score=0.890, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683795e-10, score=0.853, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=10, gamma=3.1622776601683795e-10, score=0.812, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-09, score=0.808, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-09, score=0.783, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-09, score=0.867, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-09, score=0.877, total=   0.0s
[CV] alpha=1e-11, degree=10, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=10, gamma=1e-09, score=0.860, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-11, score=0.733, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-11, score=0.637, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-11, score=0.825, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-11, score=0.765, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-11, score=0.701, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683794e-11, score=0.736, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683794e-11, score=0.642, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683794e-11, score=0.831, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683794e-11, score=0.772, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683794e-11, score=0.702, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-10, score=0.775, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-10, score=0.668, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-10, score=0.860, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-10, score=0.804, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-10, score=0.736, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683795e-10, score=0.823, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683795e-10, score=0.740, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683795e-10, score=0.890, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683795e-10, score=0.857, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=11, gamma=3.1622776601683795e-10, score=0.817, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-09, score=0.807, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-09, score=0.785, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-09, score=0.863, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-09, score=0.877, total=   0.0s
[CV] alpha=1e-11, degree=11, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=11, gamma=1e-09, score=0.862, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-11, score=0.733, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-11, score=0.637, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-11, score=0.825, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-11, score=0.765, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-11 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-11, score=0.700, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683794e-11, score=0.737, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683794e-11, score=0.642, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683794e-11, score=0.832, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683794e-11, score=0.773, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683794e-11 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683794e-11, score=0.703, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-10, score=0.781, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-10, score=0.672, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-10, score=0.864, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-10, score=0.808, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-10 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-10, score=0.742, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683795e-10, score=0.822, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683795e-10, score=0.746, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683795e-10, score=0.890, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683795e-10, score=0.860, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=3.1622776601683795e-10 ............
[CV]  alpha=1e-11, degree=12, gamma=3.1622776601683795e-10, score=0.823, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-09, score=0.807, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-09, score=0.787, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-09, score=0.860, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-09, score=0.878, total=   0.0s
[CV] alpha=1e-11, degree=12, gamma=1e-09 .............................
[CV] . alpha=1e-11, degree=12, gamma=1e-09, score=0.864, total=   0.0s

[Parallel(n_jobs=1)]: Done 375 out of 375 | elapsed:    2.8s finished

GridSearchCV(cv=5, error_score=nan,
             estimator=KernelRidge(alpha=1, coef0=1, degree=3, gamma=None,
                                   kernel='poly', kernel_params=None),
             iid='deprecated', n_jobs=None,
             param_grid={'alpha': array([1.00000000e-13, 3.16227766e-13, 1.00000000e-12, 3.16227766e-12,
       1.00000000e-11]),
                         'degree': [10, 11, 12],
                         'gamma': array([1.00000000e-11, 3.16227766e-11, 1.00000000e-10, 3.16227766e-10,
       1.00000000e-09])},
             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,
             scoring=None, verbose=3)


print('Paramètres sélectionnés:',krpoly.best_params_)
print('Score:',krpoly.score(X_test,y_test))

Paramètres sélectionnés: {'alpha': 3.162277660168379e-13, 'degree': 12, 'gamma': 3.1622776601683795e-10}
Score: 0.8530460143899636

Question 1¶

Question 2¶

Question 3¶

Question 4¶

Question 5¶