sklearn.dummy.DummyClassifier

class sklearn.dummy.DummyClassifier(strategy='stratified', random_state=None, constant=None)

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.

Parameters :

strategy: str :

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.
• “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

random_state: int seed, RandomState instance, or None (default) :

The seed of the pseudo random number generator to use.

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.

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.
outputs_2d_	bool,	True if the output at fit is 2d, else false.

Methods

fit(X, y)	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)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.

__init__(strategy='stratified', random_state=None, constant=None)

fit(X, y)

Fit the random classifier.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape = [n_samples] or [n_samples, n_outputs]

Target values.

Returns :

self : object

Returns self.

get_params(deep=True)

Get parameters for this estimator.

Parameters :

deep: boolean, optional :

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)

Perform classification on test vectors X.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Input vectors, where n_samples is the number of samples and n_features is the number of features.

Returns :

y : array, shape = [n_samples] or [n_samples, n_outputs]

Predicted target values for X.

predict_log_proba(X)

Return log probability estimates for the test vectors X.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Input vectors, where n_samples is the number of samples and n_features is the number of features.

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)

Return probability estimates for the test vectors X.

Parameters :

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Input vectors, where n_samples is the number of samples and n_features is the number of features.

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)

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

Parameters :

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples,)

True labels for X.

Returns :

score : float

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

set_params(**params)

Set the parameters of this estimator.

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

Returns :self :