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.
Parameters: strategy : str
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.
- “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
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. - 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :