[{'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:import numpy as np\nfrom sklearn.ensemble import GradientBoostingClassifier, VotingClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.pipeline import make_pipeline, make_union\nfrom sklearn.preprocessing import FunctionTransformer\ntpot_data = np.recfromcsv(\'PATH/TO/DATA/FILE\', delimiter=\'COLUMN_SEPARATOR\', dtype=np.float64)\nfeatures = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index(\'class\'), axis=1)\ntraining_features, testing_features, training_classes, testing_classes = \\\ntrain_test_split(features, tpot_data[\'class\'], random_state=42)\nexported_pipeline = make_pipeline(\nmake_union(VotingClassifier([("est", MultinomialNB(alpha=0.73, fit_prior=True))]), FunctionTransformer(lambda X: X)),\nmake_union(VotingClassifier([("est", KNeighborsClassifier(n_neighbors=5, weights="uniform"))]), FunctionTransformer(lambda X: X)),\nGradientBoostingClassifier(learning_rate=0.15, max_features=0.15, n_estimators=500)\n)\nexported_pipeline.fit(training_features, training_classes)\nresults = exported_pipeline.predict(testing_features)'}, {'role': 'assistant', 'content': "['helper_functions', 'load_data', 'modelling', 'prediction']"}]