# # ("extra_trees_clf", extra_trees_clf), # ("sgd_clf", sgd_clf), # # ("log_clf", log_clf), # ("svm_clf", svm_clf), # ("mlp_clf", mlp_clf) #] named_estimators = [ ("rand_clf_tt", rand_clf_tt), ("mlp_clf_tt", mlp_clf_tt), ("svc_clf_tt", mlp_clf_tt), ] voting_clf = VotingClassifier(named_estimators) voting_clf.fit(x_embeddings_tf_train, strat_train_set_y) sc = voting_clf.f1_score(x_embeddings_tf_test, strat_test_y) print("F1 score voting", sc) y_pred = voting_clf.predict(x_embeddings_tf_test) from sklearn.metrics import f1_score scor = f1_score(y_pred, strat_test_y) print("F1 voting", scor) #proviamo ad eliminare l'SVM #del voting_clf.estimators_[2] #del voting_clf.estimators_[3] #sc = voting_clf.score(strat_test_set, strat_test_y) #print("Score voting senza SVM",sc) #Settiamo il voting a soft