package sklearn

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package sklearn

sklearn
- Sklearn

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val get_py : string -> Py.Object.t

Get an attribute of this module as a Py.Object.t. This is useful to pass a Python function to another function.

module BaseEstimator : sig ... end

module BiclusterMixin : sig ... end

module ClassifierMixin : sig ... end

module ClusterMixin : sig ... end

module DensityMixin : sig ... end

module MetaEstimatorMixin : sig ... end

module MultiOutputMixin : sig ... end

module OutlierMixin : sig ... end

module RegressorMixin : sig ... end

module TransformerMixin : sig ... end

module Defaultdict : sig ... end

val clone : 
  ?safe:bool ->
  estimator:[> `BaseEstimator ] Np.Obj.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.

val is_classifier : [> `BaseEstimator ] Np.Obj.t -> 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.

val is_outlier_detector : [> `BaseEstimator ] Np.Obj.t -> 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.

val is_regressor : [> `BaseEstimator ] Np.Obj.t -> 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.