`sklearn.multioutput`.ClassifierChain¶

class sklearn.multioutput.ClassifierChain(base_estimator, *, order=None, cv=None, random_state=None, verbose=False)[source]¶

A multi-label model that arranges binary classifiers into a chain.

Each model makes a prediction in the order specified by the chain using all of the available features provided to the model plus the predictions of models that are earlier in the chain.

Read more in the User Guide.

New in version 0.19.

Parameters:

base_estimatorestimator

The base estimator from which the classifier chain is built.

orderarray-like of shape (n_outputs,) or ‘random’, default=None

If None, the order will be determined by the order of columns in the label matrix Y.:

order = [0, 1, 2, ..., Y.shape[1] - 1]

The order of the chain can be explicitly set by providing a list of integers. For example, for a chain of length 5.:

order = [1, 3, 2, 4, 0]

means that the first model in the chain will make predictions for column 1 in the Y matrix, the second model will make predictions for column 3, etc.

If order is random a random ordering will be used.

cvint, cross-validation generator or an iterable, default=None

Determines whether to use cross validated predictions or true labels for the results of previous estimators in the chain. Possible inputs for cv are:

None, to use true labels when fitting,
integer, to specify the number of folds in a (Stratified)KFold,
CV splitter,
An iterable yielding (train, test) splits as arrays of indices.

random_stateint, RandomState instance or None, optional (default=None)

If order='random', determines random number generation for the chain order. In addition, it controls the random seed given at each base_estimator at each chaining iteration. Thus, it is only used when base_estimator exposes a random_state. Pass an int for reproducible output across multiple function calls. See Glossary.

verbosebool, default=False

If True, chain progress is output as each model is completed.

New in version 1.2.

Attributes:

classes_list: A list of arrays of length len(estimators_) containing the class labels for each estimator in the chain.
estimators_list: A list of clones of base_estimator.
order_list: The order of labels in the classifier chain.
n_features_in_int: Number of features seen during fit. Only defined if the underlying base_estimator exposes such an attribute when fit.

New in version 0.24.
feature_names_in_ndarray of shape (n_features_in_,): Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

See also

RegressorChain: Equivalent for regression.
MultioutputClassifier: Classifies each output independently rather than chaining.

References

Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, “Classifier Chains for Multi-label Classification”, 2009.

Examples

>>> from sklearn.datasets import make_multilabel_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.multioutput import ClassifierChain
>>> X, Y = make_multilabel_classification(
...    n_samples=12, n_classes=3, random_state=0
... )
>>> X_train, X_test, Y_train, Y_test = train_test_split(
...    X, Y, random_state=0
... )
>>> base_lr = LogisticRegression(solver='lbfgs', random_state=0)
>>> chain = ClassifierChain(base_lr, order='random', random_state=0)
>>> chain.fit(X_train, Y_train).predict(X_test)
array([[1., 1., 0.],
       [1., 0., 0.],
       [0., 1., 0.]])
>>> chain.predict_proba(X_test)
array([[0.8387..., 0.9431..., 0.4576...],
       [0.8878..., 0.3684..., 0.2640...],
       [0.0321..., 0.9935..., 0.0625...]])

Methods

`decision_function`(X)	Evaluate the decision_function of the models in the chain.
`fit`(X, Y)	Fit the model to data matrix X and targets Y.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict on the data matrix X using the ClassifierChain model.
`predict_proba`(X)	Predict probability estimates.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of this estimator.

decision_function(X)[source]¶

Evaluate the decision_function of the models in the chain.

Parameters:

Xarray-like of shape (n_samples, n_features): The input data.

Returns:

Y_decisionarray-like of shape (n_samples, n_classes): Returns the decision function of the sample for each model in the chain.

fit(X, Y)[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.

Returns:

selfobject: Class instance.

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.

predict(X)[source]¶

Predict on the data matrix X using the ClassifierChain model.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The input data.

Returns:

Y_predarray-like of shape (n_samples, n_classes): The predicted values.

predict_proba(X)[source]¶

Predict probability estimates.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The input data.

Returns:

Y_probarray-like of shape (n_samples, n_classes): The predicted probabilities.

score(X, y, sample_weight=None)[source]¶

Return 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:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy of self.predict(X) w.r.t. 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.

Examples using `sklearn.multioutput.ClassifierChain`¶

Classifier Chain

sklearn.multioutput.ClassifierChain¶

Examples using sklearn.multioutput.ClassifierChain¶

`sklearn.multioutput`.ClassifierChain¶

Examples using `sklearn.multioutput.ClassifierChain`¶