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.