sklearn.compose
.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 theQuantileTransformer
or as a function and its inverse such asnp.log
andnp.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))
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. Ifregressor is None
,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 is None
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 2-dimensional array. Iffunc is None
, 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
. The function needs to return a 2-dimensional 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
n_features_in_
intNumber of features seen during fit.
- 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
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 asy
.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.
Methods
fit
(X, y, **fit_params)Fit the model according to the given training data.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X, **predict_params)Predict using the base regressor, applying inverse.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_params
(**params)Set the parameters of this estimator.
set_score_request
(*[, sample_weight])Request metadata passed to the
score
method.- 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 andn_features
is the number of features.- yarray-like of shape (n_samples,)
Target values.
- **fit_paramsdict
Parameters passed to the
fit
method of the underlying regressor.
- Returns:
- selfobject
Fitted estimator.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
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
orinverse_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
Parameters passed to the
predict
method of the underlying regressor.
- Returns:
- y_hatndarray of shape (n_samples,)
Predicted values.
- score(X, y, sample_weight=None)[source]¶
Return the coefficient of determination of the prediction.
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 ofy
, 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)
, wheren_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 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.
- set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') TransformedTargetRegressor [source]¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.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.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.
Examples using sklearn.compose.TransformedTargetRegressor
¶
Poisson regression and non-normal loss
Common pitfalls in the interpretation of coefficients of linear models
Effect of transforming the targets in regression model