package sklearn

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package sklearn
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Class type
type tag = [
  1. | `MultiOutputEstimator
]
type t = [ `BaseEstimator | `MetaEstimatorMixin | `MultiOutputEstimator | `Object ] 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 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 : ?classes:[> `ArrayLike ] Np.Obj.t list -> ?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.

classes : list of numpy arrays, shape (n_outputs) Each array is unique classes for one output in str/int Can be obtained by via ``np.unique(y[:, i]) for i in range(y.shape[1])``, where y is the target matrix of the entire dataset. This argument is required for the first call to partial_fit and can be omitted in the subsequent calls. Note that y doesn't need to contain all labels in `classes`.

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 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 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 : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.