Classifier

DummyClassifier

Multivariate Predict proba Unequal length Missing values

DummyClassifier makes predictions that ignore the input features.

This classifier serves as a simple baseline to compare against other more complex classifiers. The specific behavior of the baseline is selected with the strategy parameter.

All strategies make predictions that ignore the input feature values passed as the X argument to fit and predict. The predictions, however, typically depend on values observed in the y parameter passed to fit.

Function-identical to sklearn.dummy.DummyClassifier, which is called inside.

Schnellstart

python

from sktime.classification.dummy import DummyClassifier

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

Parameter(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.

Beispiele

>>> 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)