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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_probareturns the class prior. - ?uniform?: generates predictions uniformly at random.
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?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 seed, RandomState instance, or None (default)
The seed of the pseudo random number generator to use.
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)[source]
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fit(X, y, sample_weight=None)[source] -
Fit the random classifier.
Parameters: X : {array-like, 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 : 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
Returns self.
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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, 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.
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predict_log_proba(X)[source] -
Return log probability estimates for the test vectors X.
Parameters: X : {array-like, 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 : 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, 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 : 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, shape = (n_samples, n_features)
Test samples.
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 :
dummy.DummyClassifier()
2017-01-15 04:21:34
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