`sklearn.dummy`.DummyClassifier¶

class sklearn.dummy.DummyClassifier(strategy='warn', 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.

New in version 0.13.

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

Changed in version 0.22: The default value of strategy will change to “prior” in version 0.24. Starting from version 0.22, a warning will be raised if strategy is not explicitly set.

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-like of shape (n_outputs,)

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

Attributes

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.

Examples

>>> import numpy as np
>>> from sklearn.dummy import DummyClassifier
>>> X = np.array([-1, 1, 1, 1])
>>> y = np.array([0, 1, 1, 1])
>>> dummy_clf = DummyClassifier(strategy="most_frequent")
>>> dummy_clf.fit(X, y)
DummyClassifier(strategy='most_frequent')
>>> dummy_clf.predict(X)
array([1, 1, 1, 1])
>>> dummy_clf.score(X, y)
0.75

Methods

`fit`(X, y[, sample_weight])	Fit the random classifier.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Perform classification on test vectors X.
`predict_log_proba`(X)	Return log probability estimates for the test vectors X.
`predict_proba`(X)	Return probability estimates for the test vectors X.
`score`(X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of this estimator.

__init__(strategy='warn', random_state=None, constant=None)[source]¶: Initialize self. See help(type(self)) for accurate signature.

fit(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 of shape (n_samples,) or (n_samples, n_outputs)

Target values.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

Returns

self : object

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters: deep : bool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any

Parameter names mapped to their values.

predict(X)[source]¶

Perform classification on test vectors X.

Parameters: X : {array-like, object with finite length or shape}

Training data, requires length = n_samples
Returns: y : array-like of shape (n_samples,) or (n_samples, n_outputs)

Predicted target values for X.

predict_log_proba(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
Returns: 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(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
Returns: 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(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 of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like of shape (n_samples,), default=None

Sample weights.

Returns

score : float

Mean accuracy of self.predict(X) wrt. y.

set_params(**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.

Parameters: **params : dict

Estimator parameters.
Returns: self : object

Estimator instance.

sklearn.dummy.DummyClassifier¶

`sklearn.dummy`.DummyClassifier¶