sklearn sk0.22-0.3.0 · OCaml Package

type tag = [

| `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 ->
  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.

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 : Format.formatter -> t -> unit

Pretty-print the object to a formatter.

package sklearn