TransformedTargetRegressor#

class sklearn.compose.TransformedTargetRegressor(regressor=None, *, transformer=None, func=None, inverse_func=None, check_inverse=True)[source]#

Meta-estimator to regress on a transformed target.

Useful for applying a non-linear transformation to the target y in regression problems. This transformation can be given as a Transformer such as the QuantileTransformer or as a function and its inverse such as np.log and np.exp.

The computation during fit is:

regressor.fit(X, func(y))

or:

regressor.fit(X, transformer.transform(y))

The computation during predict is:

inverse_func(regressor.predict(X))

or:

transformer.inverse_transform(regressor.predict(X))

See also

sklearn.preprocessing.FunctionTransformer: Construct a transformer from an arbitrary callable.

Notes

Internally, the target y is always converted into a 2-dimensional array to be used by scikit-learn transformers. At the time of prediction, the output will be reshaped to a have the same number of dimensions as y.

Examples

>>> import numpy as np
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.compose import TransformedTargetRegressor
>>> tt = TransformedTargetRegressor(regressor=LinearRegression(),
...                                 func=np.log, inverse_func=np.exp)
>>> X = np.arange(4).reshape(-1, 1)
>>> y = np.exp(2 * X).ravel()
>>> tt.fit(X, y)
TransformedTargetRegressor(...)
>>> tt.score(X, y)
1.0
>>> tt.regressor_.coef_
array([2.])

For a more detailed example use case refer to Effect of transforming the targets in regression model.

fit(X, y, **fit_params)[source]#

Fit the model according to the given training data.

Parameters:

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

Training vector, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples,)

Target values.

**fit_paramsdict

If enable_metadata_routing=False (default): Parameters directly passed to the fit method of the underlying regressor.
If enable_metadata_routing=True: Parameters safely routed to the fit method of the underlying regressor.

Changed in version 1.6: See Metadata Routing User Guide for more details.

Returns:

selfobject: Fitted estimator.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Added in version 1.6.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

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, **predict_params)[source]#

Predict using the base regressor, applying inverse.

The regressor is used to predict and the inverse_func or inverse_transform is applied before returning the prediction.

Parameters:

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

Samples.

**predict_paramsdict of str -> object

If enable_metadata_routing=False (default): Parameters directly passed to the predict method of the underlying regressor.
If enable_metadata_routing=True: Parameters safely routed to the predict method of the underlying regressor.

Changed in version 1.6: See Metadata Routing User Guide for more details.

Returns:

y_hatndarray of shape (n_samples,): Predicted values.

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

Return coefficient of determination on test data.

The coefficient of determination, $R^2$, is defined as $(1 - \frac{u}{v})$, 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:

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 with 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.

Returns:

scorefloat: $R^2$ of self.predict(X) w.r.t. y.

Notes

The $R^2$ 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_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.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TransformedTargetRegressor[source]#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns: