Function random_undersample 

pub fn random_undersample(
    data: &Array2<f64>,
    targets: &Array1<f64>,
    random_seed: Option<u64>,
) -> Result<(Array2<f64>, Array1<f64>)>

Performs random undersampling to balance class distribution

Randomly removes samples from majority classes to match the minority class size. This reduces the overall dataset size but maintains balance.

Arguments

• data - Feature matrix (n_samples, n_features)
• targets - Target values for each sample
• random_seed - Optional random seed for reproducible sampling

Returns

A tuple containing the undersampled (data, targets) arrays

Examples

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_datasets::utils::random_undersample;

let data = Array2::from_shape_vec((6, 2), vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]).expect("Operation failed");
let targets = Array1::from(vec![0.0, 0.0, 1.0, 1.0, 1.0, 1.0]); // Imbalanced: 2 vs 4
let (balanced_data, balanced_targets) = random_undersample(&data, &targets, Some(42)).expect("Operation failed");
// Now both classes have 2 samples each