Struct DecisionTreeClassifier

pub struct DecisionTreeClassifier { /* private fields */ }

Implementations

impl DecisionTreeClassifier

pub fn new( max_depth: usize, samples_split: usize, metric_function: fn(x: NDArray<f64>) -> f64, ) -> DecisionTreeClassifier

Create instance of decision tree classifier

pub fn root(&self) -> &Node

Return root node of decision tree classifier tree

pub fn build_tree(&self, features: &NDArray<f64>, curr_depth: usize) -> Node

Build decision tree classifier

pub fn best_split(&self, features: NDArray<f64>) -> (f64, usize, f64)

Find optimal split for decision tree classifier

pub fn information_gain( &self, feature: NDArray<f64>, left: NDArray<f64>, right: NDArray<f64>, ) -> f64

Calculate information gain for decision tree classifier

pub fn select_max_class(&self, target: NDArray<f64>) -> f64

Select highest probability class from target output

pub fn fit(&mut self, features: &NDArray<f64>, _target: &NDArray<f64>)

Fit decision tree classifier to features and target

pub fn prediction(&self, inputs: NDArray<f64>, node: Node) -> f64

Generate prediction for row sample of decision tree classifier

pub fn predict(&self, input: NDArray<f64>) -> NDArray<f64>

Generate prediction for all row samples for decision tree classification

pub fn save(&self, filepath: &str) -> Result<()>

Save model parameters for decision tree classification

pub fn load( filepath: &str, max_depth: usize, samples_split: usize, metric_function: fn(x: NDArray<f64>) -> f64, ) -> DecisionTreeClassifier

Load model parameters for decision tree classification