[{'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:def get_feature_importance_df(feature_importances, <br>                              column_names, <br>                              top_n=25):<br>    """Get feature importance data frame.<br> <br>    Parameters<br>    ----------<br>    feature_importances : numpy ndarray<br>        Feature importances computed by an ensemble <br>            model like random forest or boosting<br>    column_names : array-like<br>        Names of the columns in the same order as feature <br>            importances<br>    top_n : integer<br>        Number of top features<br> <br>    Returns<br>    -------<br>    df : a Pandas data frame<br> <br>    """<br>     <br>    imp_dict = dict(zip(column_names, <br>                        feature_importances))<br>    top_features = sorted(imp_dict, <br>                          key=imp_dict.get, <br>                          reverse=True)[0:top_n]<br>    top_importances = [imp_dict[feature] for feature <br>                          in top_features]<br>    df = pd.DataFrame(data={\'feature\': top_features, <br>                            \'importance\': top_importances})<br>    return df'}, {'role': 'assistant', 'content': 'modelling'}]