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# `sklearn.compose`.TransformedTargetRegressor

class sklearn.compose.TransformedTargetRegressor(regressor=None, *, transformer=None, func=None, inverse_func=None, check_inverse=True)

  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 `np.log` and `np.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:
    regressor object, 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.

    transformer object, 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.

    func function, 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_func function, 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_inverse bool, 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`.

    `n_features_in_`int

      Number 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 as `y`.

  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`() | 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)

    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.

      y array-like of shape (n_samples,)

        Target values.

      **fit_params dict

        Parameters passed to the `fit` method of the underlying regressor.

    Returns:
      self object

        Fitted estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  property n_features_in_

    Number of features seen during fit.

  predict(X, **predict_params)

    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.

      **predict_params dict of str -> object

        Parameters passed to the `predict` method of the underlying regressor.

    Returns:
      y_hat ndarray of shape (n_samples,)

        Predicted values.

  score(X, y, sample_weight=None)

    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 of `y`, disregarding the input features, would get a \(R^2\) score of 0.0.

    Parameters:
      X array-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.

      y array-like of shape (n_samples,) or (n_samples, n_outputs)

        True values for `X`.

      sample_weight array-like of shape (n_samples,), default=None

        Sample weights.

    Returns:
      score float

        \(R^2\) of `self.predict(X)` w.r.t. `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)

    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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TransformedTargetRegressor

    Request metadata passed to the `score` method.

    Note that this method is only relevant if `enable_metadata_routing=True` (see `sklearn.set_config`). Please see User Guide on how the routing mechanism works.

    The options for each parameter are:

    - `True`: metadata is requested, and passed to `score` if provided. The request is ignored if metadata is not provided.

    - `False`: metadata is not requested and the meta-estimator will not pass it to `score`.

    - `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_weight str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `sample_weight` parameter in `score`.

    Returns:
      self object

        The updated object.

## Examples using `sklearn.compose.TransformedTargetRegressor`

Poisson regression and non-normal loss

Poisson regression and non-normal loss

Common pitfalls in the interpretation of coefficients of linear models

Common pitfalls in the interpretation of coefficients of linear models

Effect of transforming the targets in regression model

Effect of transforming the targets in regression model