sklearn 0.22-0.1.3 · OCaml Package

type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?n_jobs:[ `Int of int | `None ] ->
  estimator:Py.Object.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.

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.

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

val fit : ?sample_weight:Ndarray.t -> x:Py.Object.t -> y:Py.Object.t -> 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.

Returns ------- self : object

val get_params : ?deep:bool -> t -> Py.Object.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:Ndarray.t ->
  x:Py.Object.t ->
  y:Py.Object.t ->
  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:Py.Object.t -> t -> Ndarray.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:Ndarray.t ->
  x:Ndarray.t ->
  y:Ndarray.t ->
  t ->
  float

Returns 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 regression sum of squares ((y_true - y_true.mean()) ** 2).sum(). 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.

Notes ----- R^2 is calculated by weighting all the targets equally using `multioutput='uniform_average'`.

Parameters ---------- X : array-like, shape (n_samples, n_features) Test samples.

y : array-like, shape (n_samples) or (n_samples, n_outputs) True values for X.

sample_weight : array-like, shape n_samples, optional Sample weights.

Returns ------- score : float R^2 of self.predict(X) wrt. y.

val set_params : ?params:(string * Py.Object.t) list -> 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_: see constructor for documentation

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.

package sklearn