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.
Structs§
- Decision
Tree - A fitted decision tree model for classification.
- Decision
Tree Params - Decision
Tree Valid Params - The set of hyperparameters that can be specified for fitting a decision tree.
- Node
Iter - Level-order (BFT) iterator of nodes in a decision tree
- Tikz
- Struct to print a fitted decision tree in Tex using tikz and forest.
- Tree
Node - A node in the decision tree
Enums§
- Split
Quality - The metric used to determine the feature by which a node is split