ColumnEnsembleClassifier

class sktime.classification.compose.ColumnEnsembleClassifier(estimators, remainder='drop', verbose=False)[source]

Applies estimators to columns of an array or pandas DataFrame.

This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each transformer will be ensembled to form a single output.

Parameters

estimators (list of tuples) –

List of (name, transformer, column(s)) tuples specifying the transformer objects to be applied to subsets of the data.

namestring

Like in Pipeline and FeatureUnion, this allows the transformer and its parameters to be set using set_params and searched in grid search.

Estimatorestimator or {‘drop’}

Estimator must support fit and predict_proba. Special-cased strings ‘drop’ and ‘passthrough’ are accepted as well, to indicate to drop the columns

column(s) : string or int, array-like of string or int, slice, boolean mask array or callable

remainder{‘drop’, ‘passthrough’} or estimator, default ‘drop’

By default, only the specified columns in transformations are transformed and combined in the output, and the non-specified columns are dropped. (default of 'drop'). By specifying remainder='passthrough', all remaining columns that were not specified in transformations will be automatically passed through. This subset of columns is concatenated with the output of the transformations. By setting remainder to be an estimator, the remaining non-specified columns will use the remainder estimator. The estimator must support fit and transform.

__init__(estimators, remainder='drop', verbose=False)[source]

Initialize self. See help(type(self)) for accurate signature.