Decision tree learning

linfa-trees provides methods for decision tree learning algorithms.

The Big Picture

linfa-trees 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.

Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

Current state

linfa-trees currently provides an implementation of single tree fitting

Examples

There is an example in the examples/ directory showing how to use decision trees. To run, use:

$ cargo run --release --example decision_tree

This generates the following tree:

License

Dual-licensed to be compatible with the Rust project.

Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0 or the MIT license http://opensource.org/licenses/MIT, at your option. This file may not be copied, modified, or distributed except according to those terms.