DummyClassifier

DummyClassifier

class DummyClassifier(strategy='prior', random_state=None, constant=None)[source]

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.

Parameters:

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.

Attributes:

is_fitted

Whether fit has been called.

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)

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X, y)	Fit time series classifier to training data.
fit_predict(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
fit_predict_proba(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
predict(X)	Predicts labels for sequences in X.
predict_proba(X)	Predicts labels probabilities for sequences in X.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
score(X, y)	Scores predicted labels against ground truth labels on X.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.