[{'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:# We train on all the examples, using a rule of thumb for the number of iterations\n\ntb = TabularTransformer(numeric = numeric_variables,\n                        ordinal = [],\n                        lowcat  = [],\n                        highcat = categorical_variables)\n\ntb.fit(X)\nsizes = tb.shape(X)\ncategorical_levels = dict(zip(categorical_variables, sizes[1:]))\nprint(f"Input array sizes: {sizes}")\nprint(f"Categorical levels: {categorical_levels}\\\n")\n\nmodel = 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    \nmodel = compile_model(model, binary_crossentropy, [AUC(name=\'auc\'), mAP], Adam(learning_rate=0.0001))    \n\ntrain_batch = DataGenerator(X, y,\n                            tabular_transformer=tb,\n                            batch_size=BATCH_SIZE,\n                            shuffle=True)\n\nhistory = model.fit_generator(train_batch,\n                              epochs=int(np.median(best_iteration)),\n                              class_weight=[1.0, (np.sum(y==0) / np.sum(y==1))],\n                              verbose=1)\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]