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.

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.

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.

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(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=’stratified’, random_state=None, constant=None)

fit(X, y, sample_weight=None)

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.

sample_weight : array-like of shape = [n_samples], optional

Sample weights.

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, sample_weight=None)

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, shape = (n_samples, n_features)

Test samples.

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.

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

Returns:self :