sklearn.multioutput
.MultiOutputClassifier¶
-
class
sklearn.multioutput.
MultiOutputClassifier
(estimator, *, n_jobs=None)[source]¶ Multi target classification
This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classification
- Parameters
- estimatorestimator object
An estimator object implementing fit, score and predict_proba.
- n_jobsint or None, optional (default=None)
The number of jobs to run in parallel.
fit
,predict
andpartial_fit
(if supported by the passed estimator) will be parallelized for each target.When individual estimators are fast to train or predict, using
n_jobs > 1
can result in slower performance due to the parallelism overhead.None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all available processes / threads. See Glossary for more details.Changed in version 0.20:
n_jobs
default changed from 1 to None
- Attributes
- classes_ndarray of shape (n_classes,)
Class labels.
- estimators_list of
n_output
estimators Estimators used for predictions.
Examples
>>> import numpy as np >>> from sklearn.datasets import make_multilabel_classification >>> from sklearn.multioutput import MultiOutputClassifier >>> from sklearn.neighbors import KNeighborsClassifier
>>> X, y = make_multilabel_classification(n_classes=3, random_state=0) >>> clf = MultiOutputClassifier(KNeighborsClassifier()).fit(X, y) >>> clf.predict(X[-2:]) array([[1, 1, 0], [1, 1, 1]])
Methods
fit
(X, Y[, sample_weight])Fit the model to data matrix X and targets Y.
get_params
([deep])Get parameters for this estimator.
partial_fit
(X, y[, classes, sample_weight])Incrementally fit the model to data.
predict
(X)Predict multi-output variable using a model
score
(X, y)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, **fit_params)[source]¶ Fit the model to data matrix X and targets Y.
- Parameters
- X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
- Yarray-like of shape (n_samples, n_classes)
The target values.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Only supported if the underlying classifier supports sample weights.
- **fit_paramsdict of string -> object
Parameters passed to the
estimator.fit
method of each step.New in version 0.23.
- Returns
- selfobject
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
-
partial_fit
(X, y, classes=None, sample_weight=None)[source]¶ Incrementally fit the model to data. Fit a separate model for each output variable.
- Parameters
- X{array-like, sparse matrix} of shape (n_samples, n_features)
Data.
- y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets.
- classeslist of ndarray of shape (n_outputs,)
Each array is unique classes for one output in str/int Can be obtained by via
[np.unique(y[:, i]) for i in range(y.shape[1])]
, where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn’t need to contain all labels inclasses
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.
- Returns
- selfobject
-
predict
(X)[source]¶ - Predict multi-output variable using a model
trained for each target variable.
- Parameters
- X{array-like, sparse matrix} of shape (n_samples, n_features)
Data.
- Returns
- y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.
-
property
predict_proba
¶ Probability estimates. Returns prediction probabilities for each class of each output.
This method will raise a
ValueError
if any of the estimators do not havepredict_proba
.- Parameters
- Xarray-like of shape (n_samples, n_features)
Data
- Returns
- parray of shape (n_samples, n_classes), or a list of n_outputs such arrays if n_outputs > 1.
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.
Changed in version 0.19: This function now returns a list of arrays where the length of the list is
n_outputs
, and each array is (n_samples
,n_classes
) for that particular output.
-
score
(X, y)[source]¶ Returns the mean accuracy on the given test data and labels.
- Parameters
- Xarray-like of shape (n_samples, n_features)
Test samples
- yarray-like of shape (n_samples, n_outputs)
True values for X
- Returns
- scoresfloat
accuracy_score of self.predict(X) versus 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
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.