Crate linfa_trees[][src]

Expand description

Decision tree learning

linfa-trees aims to provide pure rust implementations of decison trees 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 for classification.


A fitted decision tree model for classification.

The set of hyperparameters that can be specified for fitting a decision tree.

Level-order (BFT) iterator of nodes in a decision tree

Struct to print a fitted decision tree in Tex using tikz and forest.

A node in the decision tree


The metric used to determine the feature by which a node is split

Type Definitions