package sklearn

  1. Overview
  2. Docs
Legend:
Library
Module
Module type
Parameter
Class
Class type
type tag = [
  1. | `MultiOutputRegressor
]
type t = [ `BaseEstimator | `MetaEstimatorMixin | `MultiOutputRegressor | `Object | `RegressorMixin ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val as_meta_estimator : t -> [ `MetaEstimatorMixin ] Obj.t
val as_regressor : t -> [ `RegressorMixin ] Obj.t
val create : ?n_jobs:int -> estimator:[> `BaseEstimator ] Np.Obj.t -> unit -> t

Multi target regression

This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression.

.. versionadded:: 0.18

Parameters ---------- estimator : estimator object An estimator object implementing :term:`fit` and :term:`predict`.

n_jobs : int or None, optional (default=None) The number of jobs to run in parallel for :meth:`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.

When individual estimators are fast to train or predict using `n_jobs>1` can result in slower performance due to the overhead of spawning processes.

.. versionchanged:: v0.20 `n_jobs` default changed from 1 to None

Attributes ---------- estimators_ : list of ``n_output`` estimators Estimators used for predictions.

Examples -------- >>> import numpy as np >>> from sklearn.datasets import load_linnerud >>> from sklearn.multioutput import MultiOutputRegressor >>> from sklearn.linear_model import Ridge >>> X, y = load_linnerud(return_X_y=True) >>> clf = MultiOutputRegressor(Ridge(random_state=123)).fit(X, y) >>> clf.predict(X[0]) array([176..., 35..., 57...])

val fit : ?sample_weight:[> `ArrayLike ] Np.Obj.t -> ?fit_params:(string * Py.Object.t) list -> x:[> `Spmatrix ] Np.Obj.t -> y:[> `Spmatrix ] Np.Obj.t -> [> tag ] Obj.t -> t

Fit the model to data. Fit a separate model for each output variable.

Parameters ---------- X : (sparse) array-like, shape (n_samples, n_features) Data.

y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets. An indicator matrix turns on multilabel estimation.

sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.

**fit_params : dict of string -> object Parameters passed to the ``estimator.fit`` method of each step.

Returns ------- self : object

val get_params : ?deep:bool -> [> tag ] Obj.t -> Dict.t

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.

val partial_fit : ?sample_weight:[> `ArrayLike ] Np.Obj.t -> x:[> `Spmatrix ] Np.Obj.t -> y:[> `Spmatrix ] Np.Obj.t -> [> tag ] Obj.t -> t

Incrementally fit the model to data. Fit a separate model for each output variable.

Parameters ---------- X : (sparse) array-like, shape (n_samples, n_features) Data.

y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets.

sample_weight : array-like of shape (n_samples,), default=None Sample weights. If None, then samples are equally weighted. Only supported if the underlying regressor supports sample weights.

Returns ------- self : object

val predict : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Predict multi-output variable using a model trained for each target variable.

Parameters ---------- X : (sparse) array-like, shape (n_samples, n_features) Data.

Returns ------- y : (sparse) array-like, shape (n_samples, n_outputs) Multi-output targets predicted across multiple predictors. Note: Separate models are generated for each predictor.

val score : ?sample_weight:[> `ArrayLike ] Np.Obj.t -> x:[> `ArrayLike ] Np.Obj.t -> y:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> float

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`).

val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> t

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.

val estimators_ : t -> Py.Object.t

Attribute estimators_: get value or raise Not_found if None.

val estimators_opt : t -> Py.Object.t option

Attribute estimators_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.

OCaml

Innovation. Community. Security.