sksurv.datasets.load_arff_files_standardized(path_training, attr_labels, pos_label=None, path_testing=None, survival=True, standardize_numeric=True, to_numeric=True)
Parameters: path_training : str Path to ARFF file containing data. attr_labels : sequence of str Names of attributes denoting dependent variables. If survival is set, it must be a sequence with two items: the name of the event indicator and the name of the survival/censoring time. pos_label : any type, optional Value corresponding to an event in survival analysis. Only considered if survival is True. path_testing : str, optional Path to ARFF file containing hold-out data. Only columns that are available in both training and testing are considered (excluding dependent variables). If standardize_numeric is set, data is normalized by considering both training and testing data. survival : bool, optional, default: True Whether the dependent variables denote event indicator and survival/censoring time. standardize_numeric : bool, optional, default: True Whether to standardize data to zero mean and unit variance. See sksurv.column.standardize(). to_numeric : boo, optional, default: True Whether to convert categorical variables to numeric values. See sksurv.column.categorical_to_numeric(). x_train : pandas.DataFrame, shape = (n_train, n_features) Training data. y_train : pandas.DataFrame, shape = (n_train, n_labels) Dependent variables of training data. x_test : None or pandas.DataFrame, shape = (n_train, n_features) Testing data if path_testing was provided. y_test : None or pandas.DataFrame, shape = (n_train, n_labels) Dependent variables of testing data if path_testing was provided.