[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:class TargetEncodingSmoothing(BaseEstimator, TransformerMixin):\n    def __init__(self, columns_names,k, f ):\n        self.columns_names = columns_names\n        self.learned_values = {}\n        self.dataset_mean = np.nan\n        self.k = k #\n        self.f = f #\n    def smoothing_func(self, N): #\n        return 1 / (1 + np.exp(-(N-self.k)/self.f))\n    def fit(self, X, y, **fit_params):\n        X_ = X.copy()\n        self.learned_values = {}\n        self.dataset_mean = np.mean(y)\n        X_["__target__"] = y\n        for c in [x for x in X_.columns if x in self.columns_names]:\n            stats = (X_[[c,"__target__"]]\n                     .groupby(c)["__target__"].\n                     agg([\'mean\', \'size\'])) \n            stats["alpha"] = self.smoothing_func(stats["size"])\n            stats["__target__"] = (stats["alpha"]*stats["mean"] \n                                   + (1-stats["alpha"])*self.dataset_mean)\n            stats = (stats\n                     .drop([x for x in stats.columns if x not in ["__target__",c]], axis = 1)\n                     .reset_index())\n            self.learned_values[c] = stats\n        self.dataset_mean = np.mean(y)\n        return self\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    def fit_transform(self, X, y, **fit_params):\n        self.fit(X,y)\n        return self.transform(X)\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]