Crate randomforest

Source
Expand description

Random forest classifier and regressor.

§Examples

use randomforest::criterion::Mse;
use randomforest::RandomForestRegressorOptions;
use randomforest::table::TableBuilder;

let features = [
    &[0.0, 2.0, 1.0, 0.0][..],
    &[0.0, 2.0, 1.0, 1.0][..],
    &[1.0, 2.0, 1.0, 0.0][..],
    &[2.0, 1.0, 1.0, 0.0][..],
    &[2.0, 0.0, 0.0, 0.0][..],
    &[2.0, 0.0, 0.0, 1.0][..],
    &[1.0, 0.0, 0.0, 1.0][..],
    &[0.0, 1.0, 1.0, 0.0][..],
    &[0.0, 0.0, 0.0, 0.0][..],
    &[2.0, 1.0, 0.0, 0.0][..],
    &[0.0, 1.0, 0.0, 1.0][..],
    &[1.0, 1.0, 1.0, 1.0][..],
];
let target = [
    25.0, 30.0, 46.0, 45.0, 52.0, 23.0, 43.0, 35.0, 38.0, 46.0, 48.0, 52.0
];

let mut table_builder = TableBuilder::new();
for (xs, y) in features.iter().zip(target.iter()) {
   table_builder.add_row(xs, *y)?;
}
let table = table_builder.build()?;

let regressor = RandomForestRegressorOptions::new()
    .seed(0)
    .fit(Mse, table);
assert_eq!(regressor.predict(&[1.0, 2.0, 0.0, 0.0]), 41.9785);

Modules§

  • criterion
    Criterions to measure the quality of a node split.
  • table
    Table data which contains features and a target columns.

Structs§