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 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.
- 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 : {array-like, object with finite length or shape}
Training data, requires length = n_samples
- 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
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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.
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predict
(X)[source]¶ Perform classification on test vectors X.
Parameters: - X : {array-like, 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.
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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: - 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.
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predict_proba
(X)[source]¶ Return probability estimates for the test vectors X.
Parameters: - X : {array-like, object with finite length or shape}
Training data, requires length = n_samples
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.
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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: - X : {array-like, 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 : 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.
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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