[{'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:from deep_learning_for_tabular_data.tabular import TabularTransformer, DataGenerator\n\n# Setting the CV strategy\nskf = StratifiedKFold(n_splits=FOLDS, \n                      shuffle=True, \n                      random_state=SEED)\n\n# CV Iteration\nroc_auc = list()\naverage_precision = list()\noof = np.zeros(len(X))\nbest_iteration = list()\n\nfor fold, (train_idx, test_idx) in enumerate(skf.split(X, y)):\n    \n    tb = TabularTransformer(numeric = numeric_variables,\n                        ordinal = [],\n                        lowcat  = [],\n                        highcat = categorical_variables)\n\n    tb.fit(X.iloc[train_idx])\n    sizes = tb.shape(X.iloc[train_idx])\n    categorical_levels = dict(zip(categorical_variables, sizes[1:]))\n    print(f"Input array sizes: {sizes}")\n    print(f"Categorical levels: {categorical_levels}\\\n")\n    \n    model = tabular_dnn(numeric_variables, categorical_variables,\n                        categorical_levels, \n                        feature_selection_dropout=0.1,\n                        categorical_dropout=0.1,\n                        first_dense = 256,\n                        second_dense = 256,\n                        dense_dropout = 0.1,\n                        activation_type=gelu)\n    \n    model = compile_model(model, binary_crossentropy, [AUC(name=\'auc\'), mAP], Adam(learning_rate=0.0001))\n    \n    train_batch = DataGenerator(X.iloc[train_idx], \n                                y[train_idx],\n                                tabular_transformer=tb,\n                                batch_size=BATCH_SIZE,\n                                shuffle=True)\n    \n    history = model.fit_generator(train_batch,\n                                  validation_data=(tb.transform(X.iloc[test_idx]), y[test_idx]),\n                                  epochs=30,\n                                  callbacks=[model_checkpoint, early_stopping, reduce_learning],\n                                  class_weight=[1.0, (np.sum(y==0) / np.sum(y==1))],\n                                  verbose=1)\n    \n    print("\\\nFOLD %i" % fold)\n    plot_keras_history(history, measures=[\'auc\', \'loss\'])\n    \n    best_iteration.append(np.argmax(history.history[\'val_auc\']) + 1)\n    preds = model.predict(tb.transform(X.iloc[test_idx]),\n                          verbose=1,\n                          batch_size=1024).flatten()\n\n    oof[test_idx] = preds\n\n    roc_auc.append(roc_auc_score(y_true=y[test_idx], y_score=preds))\n    average_precision.append(average_precision_score(y_true=y[test_idx], y_score=preds))\''}, {'role': 'assistant', 'content': "['evaluation', 'prediction']"}]