sklearn.multioutput
.RegressorChain¶

class
sklearn.multioutput.
RegressorChain
(base_estimator, *, order=None, cv=None, random_state=None)[source]¶ A multilabel 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.
 Parameters
 base_estimatorestimator
The base estimator from which the classifier chain is built.
 orderarraylike 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, crossvalidation 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,
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 eachbase_estimator
at each chaining iteration. Thus, it is only used whenbase_estimator
exposes arandom_state
. Pass an int for reproducible output across multiple function calls. See Glossary.
 Attributes
 estimators_list
A list of clones of base_estimator.
 order_list
The order of labels in the classifier chain.
See also
ClassifierChain
Equivalent for classification
MultioutputRegressor
Learns each output independently rather than chaining.
Methods
fit
(X, Y, **fit_params)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.
score
(X, y[, sample_weight])Return the coefficient of determination R^2 of the prediction.
set_params
(**params)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.
 Parameters
 X{arraylike, sparse matrix}, shape (n_samples, n_features)
The input data.
 Yarraylike, 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.
 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
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(X)[source]¶ Predict on the data matrix X using the ClassifierChain model.
 Parameters
 X{arraylike, sparse matrix}, shape (n_samples, n_features)
The input data.
 Returns
 Y_predarraylike, 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.
 Parameters
 Xarraylike 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.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True values for X.
 sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
R^2 of self.predict(X) wrt. y.
Notes
The R2 score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).

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
 **paramsdict
Estimator parameters.
 Returns
 selfobject
Estimator instance.