package sklearn

You can search for identifiers within the package.

in-package search v0.2.0

sklearn
- Sklearn

Legend:
Library
Module
Module type
Parameter
Class
Class type

type tag = [

| `VarianceThreshold

]

type t =
  [ `BaseEstimator
  | `Object
  | `SelectorMixin
  | `TransformerMixin
  | `VarianceThreshold ]
    Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_transformer : t -> [ `TransformerMixin ] Obj.t

val as_selector : t -> [ `SelectorMixin ] Obj.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val create : ?threshold:float -> unit -> t

Feature selector that removes all low-variance features.

This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.

Read more in the :ref:`User Guide <variance_threshold>`.

Parameters ---------- threshold : float, optional Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples.

Attributes ---------- variances_ : array, shape (n_features,) Variances of individual features.

Notes ----- Allows NaN in the input.

Examples -------- The following dataset has integer features, two of which are the same in every sample. These are removed with the default setting for threshold::

>>> X = [0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3] >>> selector = VarianceThreshold() >>> selector.fit_transform(X) array([2, 0], [1, 4], [1, 1])

val fit : ?y:Py.Object.t -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> t

Learn empirical variances from X.

Parameters ---------- X : array-like, sparse matrix, shape (n_samples, n_features) Sample vectors from which to compute variances.

y : any Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline.

Returns ------- self

val fit_transform : 
  ?y:[> `ArrayLike ] Np.Obj.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters ---------- X : numpy array of shape n_samples, n_features Training set.

y : numpy array of shape n_samples Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : numpy array of shape n_samples, n_features_new Transformed array.

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 get_support : ?indices:bool -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Get a mask, or integer index, of the features selected

Parameters ---------- indices : boolean (default False) If True, the return value will be an array of integers, rather than a boolean mask.

Returns ------- support : array An index that selects the retained features from a feature vector. If `indices` is False, this is a boolean array of shape # input features, in which an element is True iff its corresponding feature is selected for retention. If `indices` is True, this is an integer array of shape # output features whose values are indices into the input feature vector.

val inverse_transform : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Reverse the transformation operation

Parameters ---------- X : array of shape n_samples, n_selected_features The input samples.

Returns ------- X_r : array of shape n_samples, n_original_features `X` with columns of zeros inserted where features would have been removed by :meth:`transform`.

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 transform : 
  x:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.t

Reduce X to the selected features.

Parameters ---------- X : array of shape n_samples, n_features The input samples.

Returns ------- X_r : array of shape n_samples, n_selected_features The input samples with only the selected features.

val variances_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute variances_: get value or raise Not_found if None.

val variances_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute variances_: get value as an option.

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.