class sklearn.multioutput.RegressorChain(base_estimator, *, order=None, cv=None, random_state=None)[source]

A multi-label model that arranges regressions 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 0.20.


The base estimator from which the classifier chain is built.

orderarray-like of shape (n_outputs,) or ‘random’, optional

By default 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, optional (default=None)

Determines whether to use cross validated predictions or true labels for the results of previous estimators in the chain. If cv is None the true labels are used when fitting. Otherwise possible inputs for cv are:

  • 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.


A list of clones of base_estimator.


The order of labels in the classifier chain.

See also


Equivalent for classification


Learns each output independently rather than chaining.


fit(X, Y, **fit_params)

Fit the model to data matrix X and targets Y.


Get parameters for this estimator.


Predict on the data matrix X using the ClassifierChain model.

score(X, y[, sample_weight])

Return the coefficient of determination R^2 of the prediction.


Set the parameters of this estimator.

__init__(base_estimator, *, order=None, cv=None, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, Y, **fit_params)[source]

Fit the model to data matrix X and targets Y.

X{array-like, sparse matrix}, shape (n_samples, n_features)

The input data.

Yarray-like, shape (n_samples, n_classes)

The target values.

**fit_paramsdict of string -> object

Parameters passed to the fit method at each step of the regressor chain.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

paramsmapping of string to any

Parameter names mapped to their values.


Predict on the data matrix X using the ClassifierChain model.

X{array-like, sparse matrix}, shape (n_samples, n_features)

The input data.

Y_predarray-like, shape (n_samples, n_classes)

The predicted values.

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

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.

Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.


R^2 of self.predict(X) wrt. y.


The R2 score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).


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.


Estimator parameters.


Estimator instance.