Crate automl 

automl with smartcore

Train, compare, and predict with machine-learning models in Rust, using SmartCore estimators and configurable preprocessing.

Install

automl = "0.3.1"

Quickstart

Compare classifiers with cross-validation, inspect their scores, and predict with the best model:

use automl::{ClassificationModel, ClassificationSettings, DenseMatrix};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let rows: Vec<Vec<f64>> = (0..24).map(|i| vec![f64::from(i)]).collect();
    let features = DenseMatrix::from_2d_vec(&rows)?;
    let labels: Vec<u32> = (0..24).map(|i| u32::from(i >= 12)).collect();
    let settings = ClassificationSettings::default().with_number_of_folds(3);
    let mut classifier = ClassificationModel::new(features, labels, settings);
    classifier.train()?;

    println!("{classifier}");
    let predictions = classifier.predict(DenseMatrix::from_2d_array(&[&[2.0], &[20.0]])?)?;
    println!("Predictions: {predictions:?}");
    Ok(())
}

A successful train() replaces the previous results. A failed retraining attempt preserves the last successful model and fitted preprocessing; settings edits are retained so you can correct them and retry.

Models and preprocessing

• Regression: linear, ridge, lasso, elastic net, decision trees, random forests, extra trees, KNN, support vector regression, and XGBoost.
• Classification: decision trees, random forests, logistic regression, KNN, support vector classifiers, and naive Bayes variants.
• Clustering: K-means, DBSCAN, and agglomerative clustering.
• Preprocessing: scaling, imputation, categorical encoding, power transforms, column selection, PCA/SVD, interactions, and polynomial features.

Configure algorithms and pipelines through settings. Fitted preprocessing is reused for inference. See the cookbook for pipeline recipes and complete workflows.

Examples and CSV input

cargo run --example breast_cancer_csv
cargo run --example diabetes_regression

CSV loading is available without feature flags through load_csv_features and load_labeled_csv.

API documentation · Changelog

Re-exports

pub use settings::ClassificationSettings;

pub use settings::ClusteringSettings;

pub use settings::RegressionSettings;

pub use algorithms::ClassificationAlgorithm;

pub use algorithms::ClusteringAlgorithm;

pub use algorithms::RegressionAlgorithm;

pub use model::ClassificationModel;

pub use model::ClusteringModel;

pub use model::ModelError;

pub use model::ModelResult;

pub use model::RegressionModel;

pub use model::SupervisedModel;

Modules

algorithms

Algorithm enumerations and helpers. Algorithm enumerations and helpers for model training.

cookbook

A cookbook of common AutoML tasks

metrics

Metric re-exports. Re-exported clustering metrics.

model

Model definitions and implementations. Types and implementations for machine learning models.

settings

Settings customization

utils

Utility functions for the crate.

Structs

DenseMatrix

Dense matrix