[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# Training and cross-validation\nfolds = KFold(n_splits=n_fold, shuffle=True, random_state=3)\nscores = []\nfor fold_n, (train_index, valid_index) in enumerate(folds.split(X_train)):\n\\t# split training and validation data\n\\tprint(\'Fold\', fold_n, \'started at\', time.ctime())\n\\tX_tr, X_val = X_train[train_index], X_train[valid_index]\n\\tY_tr, Y_val = Y_train[train_index], Y_train[valid_index]\n\n\\t# Define the model, re-initializing for each fold\n\\tclassif_model = build_mlp_model([X_train.shape[1]],1)\n\\tclassif_model.compile(optimizer = optimizers.Adam(lr = learning_rate), loss = "binary_crossentropy")\n\\tcallbacks = [kc.EarlyStopping(monitor=\'val_loss\', patience=patience, restore_best_weights = True)]\n\n\\t# train the model\n\\tclassif_model.fit(x = X_tr, y = Y_tr, epochs = epochs, batch_size = batch_size, callbacks = callbacks, validation_data = (X_val, Y_val), verbose = 0)\n\n\\t# make predictions on validation and test data\n\\tpred_valid = classif_model.predict(x = X_val, verbose = 0)\n\n\\t# oof[valid_index] = pred_valid.reshape(-1,)\n\\tscores.append(log_loss(Y_val, pred_valid))\n    \nval_prediction = classif_model.predict(x = X_validation, verbose = 0)\n\n# Print CV scores, as well as score on the test data\nprint(\'CV mean score: {0:.4f}, std: {1:.4f}.\'.format(np.mean(scores), np.std(scores)))\nprint(scores)\nprint("Test score:", log_loss(Y_validation,val_prediction))\''}, {'role': 'assistant', 'content': "['modelling']"}]