val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create : ?threshold:float -> unit -> tFeature 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:[ `Ndarray of Ndarray.t | `SparseMatrix of Csr_matrix.t ] ->
t ->
tLearn 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:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:Ndarray.t ->
t ->
Ndarray.tFit 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 -> t -> Py.Object.tGet 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.
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.
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 -> t -> tSet 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.
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 to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.