sklearn.dummy.DummyClassifier — scikit-learn 0.20.4 documentation (original) (raw)
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. |
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| 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. |
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| 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)[source]¶
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. |
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| Returns: | self : object |
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. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
Perform classification on test vectors X.
| Parameters: | X : {array-like, object with finite length or shape} Training data, requires length = n_samples |
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| Returns: | y : array, shape = [n_samples] or [n_samples, n_outputs] Predicted target values for X. |
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 |
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| 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. |
Return probability estimates for the test vectors X.
| Parameters: | X : {array-like, object with finite length or shape} Training data, requires length = n_samples |
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| 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)[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. |
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| Returns: | score : float Mean accuracy of self.predict(X) wrt. y. |
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 |
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