I am new in sklearn and I try to learn how to use cross-validation to choose the best model of an SVM. I found this example How to split the dataset for cross validation, learning curve, and final evaluation?and I tried to understand how it does work. Here are some lines that I am not sure that I have understood.
from sklearn.learning_curve import learning_curve title = 'Learning Curves (SVM, linear kernel, $\gamma=%.6f$)' %classifier.best_estimator_.gamma estimator = SVC(kernel='linear', gamma=classifier.best_estimator_.gamma) plot_learning_curve(estimator, title, X_train, y_train, cv=cv) plt.show()
1) What is the estimator object here, is it a clone of a the best model returned by the cross_validation? I did not think!
2) Is this function
plot_learning_curve will apply the cross-validation selection again? I think yes, because it take a cross-valiation iterator.
3) What is the model that returns this score. Is it the best model selected in the section
5) of the previous link?
4)What is the utility of this operation?