[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:b"# # Split into validaion and training data on 2 features\\nrft1_train_X, rft1_val_X, rft1_train_y, rft1_val_y = train_test_split(X, y1, train_size=0.8, test_size=0.2, random_state=1)\\nrft2_train_X, rft2_val_X, rft2_train_y, rft2_val_y = train_test_split(X, y2, train_size=0.8, test_size=0.2, random_state=2)\\n\\n# Define the models\\nmodel_1 = DecisionTreeClassifier(splitter=\'best\', max_features=\'log2\', random_state=42)\\nmodel_2 = DecisionTreeClassifier(splitter=\'random\', max_features=\'log2\', random_state=42)\\nmodel_3 = DecisionTreeClassifier(splitter=\'best\', max_features=\'sqrt\', random_state=42)\\nmodel_4 = DecisionTreeClassifier(splitter=\'random\', max_features=\'sqrt\', random_state=42)\\nmodel_5 = DecisionTreeClassifier(splitter=\'random\', max_features=\'log2\', random_state=42)\\nmodel_6 = DecisionTreeClassifier(splitter=\'random\', max_features=\'sqrt\', random_state=42)\\nmodel_7 = DecisionTreeClassifier(splitter=\'best\', max_features=\'log2\', random_state=42)\\nmodel_8 = DecisionTreeClassifier(splitter=\'best\', max_features=\'sqrt\', random_state=42)\\n\\nrf_models = [model_1, model_2, model_3, model_4, model_5, model_6, model_7, model_8]\\n\\n# Function for comparing different models\\ndef score_model(model, train_X, val_X, train_y, val_y):\\n    model.fit(train_X, train_y)\\n    preds = model.predict(val_X)\\n    #accuracy = accuracy_score(y_v, preds)\\n    return mean_absolute_error(val_y, preds)\\n\\n# Evaluate the models for y1:\\nfor i in range(0, len(rf_models)):\\n    mae = score_model(rf_models[i], rft1_train_X, rft1_val_X, rft1_train_y, rft1_val_y)\\n    print(\'Model %d MAE y1: %d\' % (i+1, mae))\\n\\nprint(\'\\\\n\')\\n    \\n# Evaluate the models for y2:\\nfor i in range(0, len(rf_models)):\\n    mae = score_model(rf_models[i], rft2_train_X, rft2_val_X, rft2_train_y, rft2_val_y)\\n    print(\'Model %d MAE y2: %d\' % (i+1, mae))"'}, {'role': 'assistant', 'content': 'modelling'}]