sklearn.compose
.TransformedTargetRegressor¶

class
sklearn.compose.
TransformedTargetRegressor
(regressor=None, *, transformer=None, func=None, inverse_func=None, check_inverse=True)[source]¶ Metaestimator to regress on a transformed target.
Useful for applying a nonlinear 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 aslog
andexp
.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))
Read more in the User Guide.
New in version 0.20.
 Parameters
 regressorobject, default=None
Regressor object such as derived from
RegressorMixin
. This regressor will automatically be cloned each time prior to fitting. If regressor isNone
,LinearRegression()
is created and used. transformerobject, default=None
Estimator object such as derived from
TransformerMixin
. Cannot be set at the same time asfunc
andinverse_func
. Iftransformer
isNone
as well asfunc
andinverse_func
, the transformer will be an identity transformer. Note that the transformer will be cloned during fitting. Also, the transformer is restrictingy
to be a numpy array. funcfunction, default=None
Function to apply to
y
before passing tofit
. Cannot be set at the same time astransformer
. The function needs to return a 2dimensional array. Iffunc
isNone
, the function used will be the identity function. inverse_funcfunction, default=None
Function to apply to the prediction of the regressor. Cannot be set at the same time as
transformer
as well. The function needs to return a 2dimensional array. The inverse function is used to return predictions to the same space of the original training labels. check_inversebool, default=True
Whether to check that
transform
followed byinverse_transform
orfunc
followed byinverse_func
leads to the original targets.
 Attributes
 regressor_object
Fitted regressor.
 transformer_object
Transformer used in
fit
andpredict
.
Notes
Internally, the target
y
is always converted into a 2dimensional array to be used by scikitlearn transformers. At the time of prediction, the output will be reshaped to a have the same number of dimensions asy
.See examples/compose/plot_transformed_target.py.
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.])
Methods
fit
(X, y, **fit_params)Fit the model according to the given training data.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict using the base regressor, applying inverse.
score
(X, y[, sample_weight])Return the coefficient of determination \(R^2\) of the prediction.
set_params
(**params)Set the parameters of this estimator.

fit
(X, y, **fit_params)[source]¶ Fit the model according to the given training data.
 Parameters
 X{arraylike, 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.
 yarraylike of shape (n_samples,)
Target values.
 **fit_paramsdict
Parameters passed to the
fit
method of the underlying regressor.
 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
 paramsdict
Parameter names mapped to their values.

predict
(X)[source]¶ Predict using the base regressor, applying inverse.
The regressor is used to predict and the
inverse_func
orinverse_transform
is applied before returning the prediction. Parameters
 X{arraylike, sparse matrix} of shape (n_samples, n_features)
Samples.
 Returns
 y_hatndarray of shape (n_samples,)
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 \((1  \frac{u}{v})\), 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 ofy
, 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 with shape
(n_samples, n_samples_fitted)
, wheren_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 \(R^2\) 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
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.