sklearn.dummy.DummyClassifier

class sklearn.dummy.DummyClassifier(strategy=’stratified’, random_state=None, constant=None)
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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. 
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)
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fit(X, y, sample_weight=None)
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Fit the random classifier.
Parameters: 

X : {arraylike, object with finite length or shape} 
Training data, requires length = n_samples 
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 

get_params(deep=True)
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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)
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Perform classification on test vectors X.
Parameters: 

X : {arraylike, object with finite length or shape} 
Training data, requires length = n_samples 
Returns: 

y : array, shape = [n_samples] or [n_samples, n_outputs] 
Predicted target values for X. 

predict_log_proba(X)
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Return log probability estimates for the test vectors X.
Parameters: 

X : {arraylike, object with finite length or shape} 
Training data, requires length = n_samples 
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)
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Return probability estimates for the test vectors X.
Parameters: 

X : {arraylike, object with finite length or shape} 
Training data, requires length = n_samples 
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)
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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, None} 
Test samples with shape = (n_samples, n_features) or None. Passing None as test samples gives the same result as passing real test samples, since DummyClassifier operates independently of the sampled observations. 
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)
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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.