linfa-elasticnet provides a pure Rust implementations of elastic net linear regression.
linfa-elasticnet is a crate in the linfa ecosystem, an effort to create a toolkit for classical Machine Learning implemented in pure Rust, akin to Python's scikit-learn.
The linfa-elasticnet crate provides linear regression with ridge and LASSO constraints. The solver uses coordinate descent to find an optimal solution.
This library contains an elastic net implementation for linear regression models. It combines l1 and l2 penalties of the LASSO and ridge methods and offers therefore a greater flexibility for feature selection. With increasing penalization certain parameters become zero, their corresponding variables are dropped from the model.
See also: * Wikipedia on Elastic net
There is an usage example in the examples/ directory. To run, use:
bash
$ cargo run --features linfa/intel-mkl-system --example elasticnet
Show source code
```rust use linfa::prelude::*; use linfa_elasticnet::{ElasticNet, Result};
// load Diabetes dataset let (train, valid) = linfadatasets::diabetes().splitwith_ratio(0.90);
// train pure LASSO model with 0.1 penalty let model = ElasticNet::params() .penalty(0.3) .l1_ratio(1.0) .fit(&train)?;
println!("intercept: {}", model.intercept()); println!("params: {}", model.hyperplane());
println!("z score: {:?}", model.z_score());
// validate let yest = model.predict(&valid); println!("predicted variance: {}", valid.r2(&yest)?);
```