BaggingClassifier

Bagging ensemble of time series classifiers.

Quickstart

from sktime.classification.ensemble import BaggingClassifier

estimator = BaggingClassifier(estimator, n_estimators=10, n_samples=1.0, n_features=1.0, bootstrap=True, bootstrap_features=False, random_state=None)

Parameters(7)

estimatorsktime classifier, descendant of BaseClassifier

classifier to use in the bagging estimator

n_estimatorsint, default=10

number of estimators in the sample for bagging

n_samplesint or float, default=1.0

The number of instances drawn from X in fit to train each clone If int, then indicates number of instances precisely If float, interpreted as a fraction, and rounded by ceil

n_featuresint or float, default=1.0

The number of features/variables drawn from X in fit to train each clone If int, then indicates number of instances precisely If float, interpreted as a fraction, and rounded by ceil Note: if n_features=1, BaggingClassifier turns a univariate classifier into a multivariate classifier (as slices seen by estimator are all univariate).

bootstrapboolean, default=True

whether samples/instances are drawn with replacement (True) or not (False)

bootstrap_featuresboolean, default=False

whether features/variables are drawn with replacement (True) or not (False)

random_stateint, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Examples

>>> from sktime.classification.ensemble import BaggingClassifier
>>> from sktime.classification.kernel_based import RocketClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> X_test, y_test = load_unit_test (split = "test")
>>> clf = BaggingClassifier (
... RocketClassifier (num_kernels = 100),
... n_estimators = 10,
... )
>>> clf. fit (X_train, y_train) BaggingClassifier(
... )
>>> y_pred = clf. predict (X_test)