package sklearn

Prediction voting regressor for unfitted estimators.

.. versionadded:: 0.21

A voting regressor is an ensemble meta-estimator that fits several base regressors, each on the whole dataset. Then it averages the individual predictions to form a final prediction.

Read more in the :ref:`User Guide <voting_regressor>`.

Parameters ---------- estimators : list of (str, estimator) tuples Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones of those original estimators that will be stored in the class attribute ``self.estimators_``. An estimator can be set to ``'drop'`` using ``set_params``.

.. versionchanged:: 0.21 ``'drop'`` is accepted.

.. deprecated:: 0.22 Using ``None`` to drop an estimator is deprecated in 0.22 and support will be dropped in 0.24. Use the string ``'drop'`` instead.

weights : array-like of shape (n_regressors,), default=None Sequence of weights (`float` or `int`) to weight the occurrences of predicted values before averaging. Uses uniform weights if `None`.

n_jobs : int, default=None The number of jobs to run in parallel for ``fit``. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.

verbose : bool, default=False If True, the time elapsed while fitting will be printed as it is completed.

Attributes ---------- estimators_ : list of regressors The collection of fitted sub-estimators as defined in ``estimators`` that are not 'drop'.

named_estimators_ : Bunch Attribute to access any fitted sub-estimators by name.

.. versionadded:: 0.20

See Also -------- VotingClassifier: Soft Voting/Majority Rule classifier.

Examples -------- >>> import numpy as np >>> from sklearn.linear_model import LinearRegression >>> from sklearn.ensemble import RandomForestRegressor >>> from sklearn.ensemble import VotingRegressor >>> r1 = LinearRegression() >>> r2 = RandomForestRegressor(n_estimators=10, random_state=1) >>> X = np.array([1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]) >>> y = np.array(2, 6, 12, 20, 30, 42) >>> er = VotingRegressor(('lr', r1), ('rf', r2)) >>> print(er.fit(X, y).predict(X)) 3.3 5.7 11.8 19.7 28. 40.3

Fit the estimators.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training vectors, 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.

sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Note that this is supported only if all underlying estimators support sample weights.

Returns ------- self : object Fitted estimator.

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters ---------- X : array-like, sparse matrix, dataframe of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), default=None Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

Get the parameters of an estimator from the ensemble.

Parameters ---------- deep : bool, default=True Setting it to True gets the various classifiers and the parameters of the classifiers as well.

Predict regression target for X.

The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The input samples.

Returns ------- y : ndarray of shape (n_samples,) The 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 an estimator from the ensemble.

Valid parameter keys can be listed with `get_params()`.

Parameters ---------- **params : keyword arguments Specific parameters using e.g. `set_params(parameter_name=new_value)`. In addition, to setting the parameters of the stacking estimator, the individual estimator of the stacking estimators can also be set, or can be removed by setting them to 'drop'.

Return predictions for X for each estimator.

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) The input samples.

Returns ------- predictions: ndarray of shape (n_samples, n_classifiers) Values predicted by each regressor.

Attribute estimators_: get value or raise Not_found if None.

Attribute estimators_: get value as an option.

Attribute named_estimators_: get value or raise Not_found if None.

Attribute named_estimators_: 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.