hessboost 0.2.2

Fast, deterministic gradient boosting (GBDT) in Rust: conformal intervals, explainable boosting machines, distributional boosting, tree-based diffusion, and XGBoost model interchange
Documentation 
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//! Multiclass classification with `multi:softprob` (per-class probabilities) and
//! `predict_class` (argmax). Run: `cargo run --release --example multiclass`.

use hessboost::objective::Multiclass;
use hessboost::prelude::*;

mod common;
use common::{accuracy, fill_random, lcg};

fn main() -> Result<()> {
    // 3 classes determined by a region of two features.
    let (n, f, k) = (1500usize, 4usize, 3usize);
    let mut rng = lcg(7);
    let mut x = vec![0f32; n * f];
    let mut y = vec![0f32; n];
    fill_random(&mut rng, &mut x);
    for i in 0..n {
        let s = x[i * f] + x[i * f + 1];
        y[i] = if s < 0.7 {
            0.0
        } else if s < 1.3 {
            1.0
        } else {
            2.0
        };
    }
    let dtrain = DMatrix::from_dense(&x, n, f)?.with_labels(&y)?;

    let params = TrainingParams::builder()
        .objective(Objective::Softprob(Multiclass::new(k)?)) // required for multiclass
        .max_depth(4)
        .eta(0.2)
        .build()?;

    let model = train(&params, &dtrain, 60)?;

    // Probabilities: one row of num_class values per input row.
    let probs = model.predict(&dtrain, Iterations::Best)?;
    let row0 = probs.row(0).expect("row 0 exists");
    println!(
        "row 0 class probabilities: {row0:?} (sums to {:.3})",
        row0.iter().sum::<f32>()
    );

    // Hard predictions via argmax.
    let classes = model.predict_class(&dtrain, Iterations::Best)?;
    let acc = accuracy(classes.as_slice(), &y);
    println!("training accuracy: {acc:.3}");
    Ok(())
}