Crate linfa_trees[−][src]
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 | 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 Definitions
Result |