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 :ref:`User Guide <transformed_target_regressor>`.

.. versionadded:: 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`` 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_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``.

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.)

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 :ref:`examples/compose/plot_transformed_target.py <sphx_glr_auto_examples_compose_plot_transformed_target.py>`.

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

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 : mapping of string to any Parameter names mapped to their values.

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_hat : ndarray of shape (n_samples,) Predicted values.

Return 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 : 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, 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) wrt. y.

Notes ----- The R2 score used when calling ``score`` on a regressor uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).

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.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

Attribute regressor_: get value or raise Not_found if None.

Attribute regressor_: get value as an option.

Attribute transformer_: get value or raise Not_found if None.

Attribute transformer_: get value as an option.

Print the object to a human-readable representation.

Print the object to a human-readable representation.

Pretty-print the object to a formatter.