DummyClassifier

DummyClassifier makes predictions that ignore the input features.

Quickstart

from sktime.classification.dummy import DummyClassifier

estimator = DummyClassifier(strategy='prior', random_state=None, constant=None)

Parameters(3)

strategy{“most_frequent”, “prior”, “stratified”, “uniform”, “constant”}, default=”prior”

Strategy to use to generate predictions.

• “most_frequent”: the predict method always returns the most frequent class label in the observed y argument passed to fit. The predict_proba method returns the matching one-hot encoded vector.

• “prior”: the predict method always returns the most frequent class label in the observed y argument passed to fit (like “most_frequent”). predict_proba always returns the empirical class distribution of y also known as the empirical class prior distribution.

• “stratified”: the predict_proba method randomly samples one-hot vectors from a multinomial distribution parametrized by the empirical class prior probabilities. The predict method returns the class label which got probability one in the one-hot vector of predict_proba. Each sampled row of both methods is therefore independent and identically distributed.

• “uniform”: generates predictions uniformly at random from the list of unique classes observed in y, i.e. each class has equal probability.

• “constant”: always predicts a constant label that is provided by the user. This is useful for metrics that evaluate a non-majority class.

random_stateint, RandomState instance or None, default=None

Controls the randomness to generate the predictions when strategy='stratified' or strategy='uniform'. Pass an int for reproducible output across multiple function calls.

constantint or str or array-like of shape (n_outputs,), default=None

The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy.

Examples

>>> from sktime.classification.dummy import DummyClassifier
>>> 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")
>>> classifier = DummyClassifier (strategy = "prior")
>>> classifier. fit (X_train, y_train) DummyClassifier()
>>> y_pred = classifier. predict (X_test)
>>> y_pred_proba = classifier. predict_proba (X_test)