In [69]:

    

xLen = len(X_train_scaled)
tSize = [.1, .2, .3, .4, .5]
train_sizes, train_scores, valid_scores = learning_curve(SVR(), X_train_scaled, y_train, train_sizes = tSize, n_jobs = -1, verbose = 3)

train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
valid_scores_mean = np.mean(valid_scores, axis=1)
valid_scores_std = np.std(valid_scores, axis=1)

plt.grid()

plt.title("Validation Curve - SVR")
plt.fill_between(tSize, train_scores_mean - train_scores_std,
                 train_scores_mean + train_scores_std, alpha=0.1,
                 color="darkorange")
plt.fill_between(tSize, valid_scores_mean - valid_scores_std,
                 valid_scores_mean + valid_scores_std, alpha=0.1, color="navy")
plt.plot(tSize, train_scores_mean, 'o-', color="darkorange",
         label="Training score")
plt.plot(tSize, valid_scores_mean, 'o-', color="navy",
         label="Cross-validation score")

plt.legend(loc="best")
plt.show


    

    




[learning_curve] Training set sizes: [17529 35059 52588 70118 87648]






    




[Parallel(n_jobs=-1)]: Done   4 out of  15 | elapsed:  7.0min remaining: 19.2min
[Parallel(n_jobs=-1)]: Done  10 out of  15 | elapsed: 20.1min remaining: 10.0min
[Parallel(n_jobs=-1)]: Done  15 out of  15 | elapsed: 42.0min finished






    
Out[69]:





<function matplotlib.pyplot.show>