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§
- Criterions to measure the quality of a node split.
- Table data which contains features and a target columns.
Structs§
- Random forest classifier.
- Random forest options.
- Random forest regressor.
- Random forest options.