`sklearn.dummy`.DummyClassifier¶

class sklearn.dummy.DummyClassifier(*, strategy='prior', 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{“stratified”, “most_frequent”, “prior”, “uniform”, “constant”}, default=”prior”

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 0.24: The default value of strategy has changed to “prior” in version 0.24.

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. See Glossary.

constantint 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_ndarray of shape (n_classes,) or list of such arrays: Class labels for each output.
n_classes_int or list of int: Number of label for each output.
class_prior_ndarray of shape (n_classes,) or list of such arrays: 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.

fit(X, y, sample_weight=None)[source]¶

Fit the random classifier.

Parameters

Xarray-like of shape (n_samples, n_features): Training data.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): Target values.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

selfobject

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict: Parameter names mapped to their values.

predict(X)[source]¶

Perform classification on test vectors X.

Parameters

Xarray-like of shape (n_samples, n_features): Test data.

Returns

yarray-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

Pndarray of shape (n_samples, n_classes) or list of such arrays: 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

Xarray-like of shape (n_samples, n_features): Test data.

Returns

Pndarray of shape (n_samples, n_classes) or list of such arrays: 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

XNone or array-like of shape (n_samples, n_features): Test samples. Passing None as test samples gives the same result as passing real test samples, since DummyClassifier operates independently of the sampled observations.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns

scorefloat: 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 Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**paramsdict: Estimator parameters.

Returns

selfestimator instance: Estimator instance.

sklearn.dummy.DummyClassifier¶

`sklearn.dummy`.DummyClassifier¶