package sklearn

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package sklearn
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Module Sklearn.BaseSource

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
Sourceval 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.

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