val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create :
?regressor:Py.Object.t ->
?transformer:Py.Object.t ->
?func:Py.Object.t ->
?inverse_func:Py.Object.t ->
?check_inverse:bool ->
unit ->
tMeta-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>`.
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 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, optional 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, 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 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>`.
val fit :
?fit_params:(string * Py.Object.t) list ->
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
y:Ndarray.t ->
t ->
tFit the model according to the given training data.
Parameters ---------- X : array-like, 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 : array-like, shape (n_samples,) Target values.
**fit_params : dict of string -> object Parameters passed to the ``fit`` method of the underlying regressor.
Returns ------- self : object
val get_params : ?deep:bool -> t -> Py.Object.tGet 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.
val predict :
x:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
Ndarray.tPredict 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 : array, 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 will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
val set_params : ?params:(string * Py.Object.t) list -> t -> tSet 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.
val regressor_ : t -> Py.Object.tAttribute regressor_: see constructor for documentation
val transformer_ : t -> Py.Object.tAttribute transformer_: see constructor for documentation
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.