[{'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 sklearn.base import BaseEstimator, TransformerMixin\nclass TargetEncoding(BaseEstimator, TransformerMixin):\n    def __init__(self, columns_names ):\n        self.columns_names = columns_names\n        self.learned_values = {}\n        self.dataset_mean = np.nan\n    \n    def fit(self, X, y, **fit_params):\n        X_ = X.copy()\n        self.learned_values = {}\n        X_["__target__"] = y\n        for c in [x for x in X_.columns if x in self.columns_names]:\n            self.learned_values[c] = (X_[[c,"__target__"]]\n                                      .groupby(c)["__target__"].mean()\n                                      .reset_index())\n        self.dataset_mean = np.mean(y)\n        return self\n    \n    def transform(self, X, **fit_params):\n        transformed_X = X[self.columns_names].copy()\n        for c in transformed_X.columns:\n            transformed_X[c] = (transformed_X[[c]]\n                                .merge(self.learned_values[c], on = c, how = \'left\')\n                               )["__target__"]\n        transformed_X = transformed_X.fillna(self.dataset_mean)\n        return transformed_X\n    \n    def fit_transform(self, X, y, **fit_params):\n        self.fit(X,y)\n        return self.transform(X)\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]