Jointly tuning gamma and C with GridSearchCV

In the previous exercise the best value of gamma was 0.001 using the default value of C, which is 1. In this exercise you'll search for the best combination of C and gamma using GridSearchCV.

As in the previous exercise, the 2-vs-not-2 digits dataset is already loaded, but this time it's split into the variables X_train, y_train, X_test, and y_test. Even though cross-validation already splits the training set into parts, it's often a good idea to hold out a separate test set to make sure the cross-validation results are sensible.

This exercise is part of the course

Linear Classifiers in Python

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Exercise instructions

Run GridSearchCV to find the best hyperparameters using the training set.
Print the best values of the parameters.
Print out the accuracy on the test set, which was not used during the cross-validation procedure.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Instantiate an RBF SVM
svm = SVC()

# Instantiate the GridSearchCV object and run the search
parameters = {'C':[0.1, 1, 10], 'gamma':[0.00001, 0.0001, 0.001, 0.01, 0.1]}
searcher = GridSearchCV(svm, ____)
____.fit(____)

# Report the best parameters and the corresponding score
print("Best CV params", searcher.best_params_)
print("Best CV accuracy", searcher.best_score_)

# Report the test accuracy using these best parameters
print("Test accuracy of best grid search hypers:", searcher.score(____))

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