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 the QuantileTransformer or as a function and its inverse such as log and 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. If regressor is None, LinearRegression() is created and used.  
transformerobject, default=None 

Estimator object such as derived from TransformerMixin. Cannot be set at the same time as func and inverse_func. If transformer is None as well as func and inverse_func, the transformer will be an identity transformer. Note that the transformer will be cloned during fitting. Also, the transformer is restricting y to be a numpy array.  
funcfunction, default=None 

Function to apply to y before passing to fit. Cannot be set at the same time as transformer. The function needs to return a 2-dimensional array. If func 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 as well. 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 by inverse_transform or func followed by inverse_func leads to the original targets.    Attributes 
 
regressor_object 

Fitted regressor.  
transformer_object 

Transformer used in fit and predict.     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. 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{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 

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 or inverse_transform is applied before returning the prediction.  Parameters 
 
X{array-like, 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 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) wrt. 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.     
 
 Examples using sklearn.compose.TransformedTargetRegressor
 
  Poisson regression and non-normal loss  

  Common pitfalls in interpretation of coefficients of linear models  

  Effect of transforming the targets in regression model