Using SGDClassifier
In this final coding exercise, you'll do a hyperparameter search over the regularization strength and the loss (logistic regression vs. linear SVM) using SGDClassifier().
This exercise is part of the course
Linear Classifiers in Python
Exercise instructions
- Instantiate an
SGDClassifierinstance withrandom_state=0. - Search over the regularization strength and the
hingevs.log_losslosses.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# We set random_state=0 for reproducibility
linear_classifier = ____(random_state=0)
# Instantiate the GridSearchCV object and run the search
parameters = {'alpha':[0.00001, 0.0001, 0.001, 0.01, 0.1, 1],
'loss':[____]}
searcher = GridSearchCV(linear_classifier, parameters, cv=10)
searcher.fit(X_train, y_train)
# Report the best parameters and the corresponding score
print("Best CV params", searcher.best_params_)
print("Best CV accuracy", searcher.best_score_)
print("Test accuracy of best grid search hypers:", searcher.score(X_test, y_test))