Building a Sentiment Classifier using Scikit-Learn

5.hyper_parameter_tuning_2.py

# Phase 2: penalty and alpha

clf = SGDClassifier()

distributions = dict(
    penalty=['l1', 'l2', 'elasticnet'],
    alpha=uniform(loc=1e-6, scale=1e-4)
)

random_search_cv = RandomizedSearchCV(
    estimator=clf,
    param_distributions=distributions,
    cv=5,
    n_iter=50
)
random_search_cv.fit(X_train, y_train)
print(f'Best params: {random_search_cv.best_params_}')
print(f'Best score: {random_search_cv.best_score_}')