Struct DecisionTreeClassifierParameters 

pub struct DecisionTreeClassifierParameters {
    pub criterion: SplitCriterion,
    pub max_depth: Option<u16>,
    pub min_samples_leaf: usize,
    pub min_samples_split: usize,
    pub seed: Option<u64>,
}

Parameters of Decision Tree

Fields

criterion: SplitCriterion

Split criteria to use when building a tree.

max_depth: Option<u16>

The maximum depth of the tree.

min_samples_leaf: usize

The minimum number of samples required to be at a leaf node.

min_samples_split: usize

The minimum number of samples required to split an internal node.

seed: Option<u64>

Controls the randomness of the estimator

Implementations

impl DecisionTreeClassifierParameters

pub fn with_criterion(self, criterion: SplitCriterion) -> Self

Split criteria to use when building a tree.

pub fn with_max_depth(self, max_depth: u16) -> Self

The maximum depth of the tree.

pub fn with_min_samples_leaf(self, min_samples_leaf: usize) -> Self

The minimum number of samples required to be at a leaf node.

pub fn with_min_samples_split(self, min_samples_split: usize) -> Self

The minimum number of samples required to split an internal node.

Trait Implementations

impl Clone for DecisionTreeClassifierParameters

fn clone(&self) -> DecisionTreeClassifierParameters

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for DecisionTreeClassifierParameters

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for DecisionTreeClassifierParameters

fn default() -> Self

Returns the “default value” for a type.

impl<TX: Number + PartialOrd, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, DecisionTreeClassifierParameters> for DecisionTreeClassifier<TX, TY, X, Y>

fn new() -> Self

Empty constructor, instantiate an empty estimator. Object is dropped as soon as fit() is called. used to pass around the correct fit() implementation. by calling ::fit(). mostly used to be used with model_selection::cross_validate(...)

fn fit( x: &X, y: &Y, parameters: DecisionTreeClassifierParameters, ) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model’s parameters.