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
.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_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.
- 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_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.
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