Struct VarianceThreshold 

pub struct VarianceThreshold {
    pub threshold: f64,
}

Parameters for VarianceThreshold feature selector (unfitted state).

Removes all features whose variance does not meet a minimum threshold. By default (threshold = 0.0), it removes features that have zero variance, i.e., features that are constant across all samples.

This is a simple baseline approach to feature selection: a feature with higher variance is more likely to be informative (though this is not guaranteed).

Example

use anofox_ml_preprocessing::VarianceThreshold;
use anofox_ml_core::{FitUnsupervised, Transform};
use ndarray::array;

let x = array![
    [0.0, 2.0, 0.0],
    [0.0, 4.0, 0.0],
    [0.0, 6.0, 0.0],
];

// Remove zero-variance features (columns 0 and 2 are constant)
let selector = VarianceThreshold::new(0.0);
let fitted = FitUnsupervised::<f64>::fit(&selector, &x).unwrap();
let x_selected = fitted.transform(&x).unwrap();

assert_eq!(x_selected.ncols(), 1); // only the varying column survives

Fields

threshold: f64

Minimum variance required for a feature to be kept. Features with variance <= threshold are removed.

Implementations

impl VarianceThreshold

pub fn new(threshold: f64) -> Self

Create a new VarianceThreshold with the given threshold.

A threshold of 0.0 removes only constant (zero-variance) features.

pub fn with_threshold(self, threshold: f64) -> Self

Set the variance threshold.

Trait Implementations

impl Clone for VarianceThreshold

fn clone(&self) -> VarianceThreshold

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for VarianceThreshold

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for VarianceThreshold

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for VarianceThreshold

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<F: Float> FitUnsupervised<F> for VarianceThreshold

type Fitted = FittedVarianceThreshold<F>

fn fit(&self, x: &Array2<F>) -> Result<Self::Fitted>

impl Serialize for VarianceThreshold

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.