sklearn.compose
.TransformedTargetRegressor¶

class
sklearn.compose.
TransformedTargetRegressor
(regressor=None, transformer=None, func=None, inverse_func=None, check_inverse=True)¶ Metaestimator to regress on a transformed target.
Useful for applying a nonlinear transformation in regression problems. This transformation can be given as a Transformer such as the QuantileTransformer or as a function and its inverse such as
log
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.
Parameters: regressor : object, default=LinearRegression()
Regressor object such as derived from
RegressorMixin
. This regressor will automatically be cloned each time prior to fitting.transformer : object, 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.func : function, optional
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_func : function, optional
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_inverse : bool, 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) # doctest: +ELLIPSIS TransformedTargetRegressor(...) >>> tt.score(X, y) 1.0 >>> tt.regressor_.coef_ array([2.])
Methods
fit
(X, y[, sample_weight])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])Returns the coefficient of determination R^2 of the prediction. set_params
(**params)Set the parameters of this estimator. 
__init__
(regressor=None, transformer=None, func=None, inverse_func=None, check_inverse=True)¶ Initialize self. See help(type(self)) for accurate signature.

fit
(X, y, sample_weight=None)¶ Fit the model according to the given training data.
Parameters: X : {arraylike, sparse matrix}, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features.
y : arraylike, shape (n_samples,)
Target values.
sample_weight : arraylike, shape (n_samples,) optional
Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.
Returns: self : object

get_params
(deep=True)¶ Get parameters for this estimator.
Parameters: deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.

predict
(X)¶ 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}, shape = (n_samples, n_features)
Samples.
Returns: y_hat : array, shape = (n_samples,)
Predicted values.

score
(X, y, sample_weight=None)¶ Returns 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: X : arraylike, shape = (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix instead, shape = (n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for the estimator.
y : arraylike, shape = (n_samples) or (n_samples, n_outputs)
True values for X.
sample_weight : arraylike, shape = [n_samples], optional
Sample weights.
Returns: score : float
R^2 of self.predict(X) wrt. y.

set_params
(**params)¶ 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.Returns: self
