RF_hyperparameters search

best_model = None
best_acc = 0
best_f1 = 0
best_depth = 0
best_est = 0

for est in range(1, 50):
    for depth in range (1, 16):
        model_forest = RandomForestClassifier(random_state=42, n_estimators=est, max_depth=depth, class_weight='balanced')
        model_forest.fit(X_train_counts, y_train)
        predictions_rf = model_forest.predict(X_test_counts)
        acc_score = accuracy_score(predictions_rf, y_test)
        f1score = f1_score(predictions_rf, y_test, pos_label=None, average='weighted')
        if f1score > best_f1:
            best_model = model_forest
            best_f1 = f1score
            best_est = est
            best_depth = depth
print("F1:", best_f1, "Количество деревьев:", best_est, "Максимальная глубина:", best_depth)