Base (p.sklearn.0.22-0.1.3.doc.sklearn.Sklearn.Base)

Sourcemodule BaseEstimator : sig ... end

Sourcemodule BiclusterMixin : sig ... end

Sourcemodule ClassifierMixin : sig ... end

Sourcemodule ClusterMixin : sig ... end

Sourcemodule DensityMixin : sig ... end

Sourcemodule MetaEstimatorMixin : sig ... end

Sourcemodule MultiOutputMixin : sig ... end

Sourcemodule OutlierMixin : sig ... end

Sourcemodule RegressorMixin : sig ... end

Sourcemodule TransformerMixin : sig ... end

Source

val clone : 
  ?safe:bool ->
  estimator:
    [ `Estimator of Py.Object.t
    | `ArrayLike of Py.Object.t
    | `PyObject of Py.Object.t ] ->
  unit ->
  Py.Object.t

Constructs a new estimator with the same parameters.

Clone does a deep copy of the model in an estimator without actually copying attached data. It yields a new estimator with the same parameters that has not been fit on any data.

Parameters ---------- estimator : estimator object, or list, tuple or set of objects The estimator or group of estimators to be cloned

safe : boolean, optional If safe is false, clone will fall back to a deep copy on objects that are not estimators.

Sourceval is_classifier : estimator:Py.Object.t -> unit -> bool

Return True if the given estimator is (probably) a classifier.

Parameters ---------- estimator : object Estimator object to test.

Returns ------- out : bool True if estimator is a classifier and False otherwise.

Sourceval is_outlier_detector : estimator:Py.Object.t -> unit -> bool

Return True if the given estimator is (probably) an outlier detector.

Parameters ---------- estimator : object Estimator object to test.

Returns ------- out : bool True if estimator is an outlier detector and False otherwise.

Sourceval is_regressor : estimator:Py.Object.t -> unit -> bool

Return True if the given estimator is (probably) a regressor.

Parameters ---------- estimator : object Estimator object to test.

Returns ------- out : bool True if estimator is a regressor and False otherwise.