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
estimator : estimator object
An estimator object implementing fit, score and predict_proba.
n_jobs : int or None, optional (default=None)
The number of jobs to use for the computation. It does each target variable in y in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.
Attributes
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.
-
__init__
(estimator, n_jobs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
fit
(X, Y, sample_weight=None)[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.
Y : array-like of shape (n_samples, n_classes)
The target values.
sample_weight : array-like of shape (n_samples,) or None
Sample weights. If None, then samples are equally weighted. Only supported if the underlying classifier supports sample weights.
- Returns
self : object
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
deep : bool, default=True
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.
-
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 : (sparse) array-like, shape (n_samples, n_features)
Data.
y : (sparse) array-like, shape (n_samples, n_outputs)
Multi-output targets.
classes : list of numpy arrays, 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_weight : array-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
self : object
-
predict
(X)[source]¶ - Predict multi-output variable using a model
trained for each target variable.
- Parameters
X : (sparse) array-like, shape (n_samples, n_features)
Data.
- Returns
y : (sparse) array-like, 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
X : array-like, shape (n_samples, n_features)
Data
- Returns
p : array 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_.
-
score
(X, y)[source]¶ Returns the mean accuracy on the given test data and labels.
- Parameters
X : array-like, shape [n_samples, n_features]
Test samples
y : array-like, shape [n_samples, n_outputs]
True values for X
- Returns
scores : float
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 pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params : dict
Estimator parameters.
- Returns
self : object
Estimator instance.