# sklearn.dummy.DummyClassifier¶

class sklearn.dummy.DummyClassifier(strategy=’stratified’, random_state=None, constant=None)[source]

DummyClassifier is a classifier that makes predictions using simple rules.

This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.

Read more in the User Guide.

Parameters: strategy : str, default=”stratified” Strategy to use to generate predictions. “stratified”: generates predictions by respecting the training set’s class distribution. “most_frequent”: always predicts the most frequent label in the training set. “prior”: always predicts the class that maximizes the class prior (like “most_frequent”) and predict_proba returns the class prior. “uniform”: generates predictions uniformly at random. “constant”: always predicts a constant label that is provided by the user. This is useful for metrics that evaluate a non-majority class New in version 0.17: Dummy Classifier now supports prior fitting strategy using parameter prior. random_state : int, 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. constant : int or str or array of shape = [n_outputs] The explicit constant as predicted by the “constant” strategy. This parameter is useful only for the “constant” strategy. classes_ : array or list of array of shape = [n_classes] Class labels for each output. n_classes_ : array or list of array of shape = [n_classes] Number of label for each output. class_prior_ : array or list of array of shape = [n_classes] Probability of each class for each output. n_outputs_ : int, Number of outputs. sparse_output_ : bool, True if the array returned from predict is to be in sparse CSC format. Is automatically set to True if the input y is passed in sparse format.

Methods

 fit(self, X, y[, sample_weight]) Fit the random classifier. get_params(self[, deep]) Get parameters for this estimator. predict(self, X) Perform classification on test vectors X. predict_log_proba(self, X) Return log probability estimates for the test vectors X. predict_proba(self, X) Return probability estimates for the test vectors X. score(self, X, y[, sample_weight]) Returns the mean accuracy on the given test data and labels. set_params(self, \*\*params) Set the parameters of this estimator.
__init__(self, strategy=’stratified’, random_state=None, constant=None)[source]
fit(self, X, y, sample_weight=None)[source]

Fit the random classifier.

Parameters: X : {array-like, object with finite length or shape} Training data, requires length = n_samples y : array-like, shape = [n_samples] or [n_samples, n_outputs] Target values. sample_weight : array-like of shape = [n_samples], optional Sample weights. self : object
get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters: deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. params : mapping of string to any Parameter names mapped to their values.
predict(self, X)[source]

Perform classification on test vectors X.

Parameters: X : {array-like, object with finite length or shape} Training data, requires length = n_samples y : array, shape = [n_samples] or [n_samples, n_outputs] Predicted target values for X.
predict_log_proba(self, X)[source]

Return log probability estimates for the test vectors X.

Parameters: X : {array-like, object with finite length or shape} Training data, requires length = n_samples P : array-like or list of array-like of shape = [n_samples, n_classes] Returns the log probability of the sample for each class in the model, where classes are ordered arithmetically for each output.
predict_proba(self, X)[source]

Return probability estimates for the test vectors X.

Parameters: X : {array-like, object with finite length or shape} Training data, requires length = n_samples P : array-like or list of array-lke of shape = [n_samples, n_classes] Returns the probability of the sample for each class in the model, where classes are ordered arithmetically, for each output.
score(self, X, y, sample_weight=None)[source]

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters: X : {array-like, None} Test samples with shape = (n_samples, n_features) or None. Passing None as test samples gives the same result as passing real test samples, since DummyClassifier operates independently of the sampled observations. y : array-like, shape = (n_samples) or (n_samples, n_outputs) True labels for X. sample_weight : array-like, shape = [n_samples], optional Sample weights. score : float Mean accuracy of self.predict(X) wrt. y.
set_params(self, **params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns: self