_df[dis_features] = imp_discrete.fit_transform(_df[dis_features].values)# for continuous features we will use 'mean' strategyimp_continuous = Imputer(missing_values='NaN', strategy='mean', axis=0) _df[cont_features] = imp_continuous.fit_transform(_df[cont_features].values)return_df 开发...
transform_utils azureml.automl.runtime.featurizer.transformer.timeseries.unique_target_grain_dropper azureml.automl.runtime.featurizer.transformer.timeseries.unique_target_grain_dropper_base azureml.automl.runtime.fit_output azureml.automl.runtime.fit_pipeline azureml.aut...
'maxabs_scale', 'minmax_scale', 'label_binarize', 'quantile_transform', 'power_transform', 'Imputer' ] Sign up for freeto join this conversation on GitHub. Already have an account?Sign in to comment Labels bugSomething isn't working...
# 需要导入模块: from sklearn.preprocessing.imputation import Imputer [as 别名]# 或者: from sklearn.preprocessing.imputation.Imputer importfit_transform[as 别名]defcheck_indicator(X, expected_imputed_features, axis):n_samples, n_features = X.shape imputer = Imputer(missing_values=-1, strategy='m...