Crate linfa_trees

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.

Structs

• DecisionTree
  A fitted decision tree model for classification.
• DecisionTreeParams
• DecisionTreeValidParams
  The set of hyperparameters that can be specified for fitting a decision tree.
• NodeIter
  Level-order (BFT) iterator of nodes in a decision tree
• Tikz
  Struct to print a fitted decision tree in Tex using tikz and forest.
• TreeNode
  A node in the decision tree

Enums

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

Type Aliases

• Result