RandomIntervalClassifier

Random Interval Classifier.

Quickstart

from sktime.classification.feature_based import RandomIntervalClassifier

estimator = RandomIntervalClassifier(n_intervals=100, interval_transformers=None, estimator=None, n_jobs=1, random_state=None)

Parameters(5)

n_intervalsint, default=100,

The number of intervals of random length, position and dimension to be extracted.

interval_transformerstransformer or list of transformers, default=None,

Transformer(s) used to extract features from each interval. If None, defaults to the Catch22 transformer.

estimatorsklearn classifier, default=None

An sklearn estimator to be built using the transformed data. Defaults to a Rotation Forest with 200 trees.

n_jobsint, default=1

The number of jobs to run in parallel for both fit and predict. -1 means using all processors.

random_stateint or None, default=None

Seed for random, integer.

Examples

>>> from sktime.classification.feature_based import RandomIntervalClassifier
>>> from sklearn.ensemble import RandomForestClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train", return_X_y = True)
>>> X_test, y_test = load_unit_test (split = "test", return_X_y = True)
>>> clf = RandomIntervalClassifier (
... estimator = RandomForestClassifier (n_estimators = 5)
... )
>>> clf. fit (X_train, y_train) RandomIntervalClassifier(
... )
>>> y_pred = clf. predict (X_test)