Struct TreeClassifierParams 

pub struct TreeClassifierParams {
    pub base_params: TreeParams,
    pub criterion: String,
}

Struct representing the parameters for a decision tree classifier.

Fields

base_params: TreeParams

criterion: String

Implementations

impl TreeClassifierParams

pub fn new() -> Self

Creates a new instance of TreeClassifierParams with default values.

pub fn set_min_samples_split( &mut self, min_samples_split: u16, ) -> Result<(), Box<dyn Error>>

Sets the minimum number of samples required to split a node.

Arguments

• min_samples_split - The minimum number of samples to split.

Errors

Returns an error if min_samples_split is less than 2.

pub fn set_max_depth( &mut self, max_depth: Option<u16>, ) -> Result<(), Box<dyn Error>>

Sets the maximum depth of the decision tree.

Arguments

• max_depth - The maximum depth of the tree.

Errors

Returns an error if max_depth is less than 1.

pub fn set_criterion(&mut self, criterion: String) -> Result<(), Box<dyn Error>>

Sets the criterion used for splitting nodes in the decision tree.

Arguments

• criterion - The criterion for splitting nodes.

Errors

Returns an error if criterion is not “gini” or “entropy”.

pub fn min_samples_split(&self) -> u16

Returns the minimum number of samples required to split a node.

pub fn max_depth(&self) -> Option<u16>

Returns the maximum depth of the decision tree.

pub fn criterion(&self) -> &str

Returns the criterion used for splitting nodes in the decision tree.

Trait Implementations

impl Clone for TreeClassifierParams

fn clone(&self) -> TreeClassifierParams

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TreeClassifierParams

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

Formats the value using the given formatter.

impl Default for TreeClassifierParams

fn default() -> Self

Creates a new instance of TreeClassifierParams with default values.