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 nonmajority 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. 
fit
(X, y, sample_weight=None)[source]¶ Fit the random classifier.
Parameters:  X : {arraylike, 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 : arraylike, shape = [n_samples] or [n_samples, n_outputs]
Target values.
 sample_weight : arraylike of shape = [n_samples], optional
Sample weights.
Returns:  self : object
Returns self.

get_params
(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.
Returns:  params : mapping of string to any
Parameter names mapped to their values.

predict
(X)[source]¶ Perform classification on test vectors X.
Parameters:  X : {arraylike, 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)[source]¶ Return log probability estimates for the test vectors X.
Parameters:  X : {arraylike, 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 : arraylike or list of arraylike 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 : {arraylike, 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 : arraylike or list of arraylke 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 multilabel 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 : arraylike, shape = (n_samples, n_features)
Test samples.
 y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
 sample_weight : arraylike, shape = [n_samples], optional
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.Returns:  self