Struct smartcore::tree::decision_tree_regressor::DecisionTreeRegressorParameters

pub struct DecisionTreeRegressorParameters {
    pub max_depth: Option<u16>,
    pub min_samples_leaf: usize,
    pub min_samples_split: usize,
}

Parameters of Regression Tree

Fields

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.

Implementations

impl DecisionTreeRegressorParameters

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 DecisionTreeRegressorParameters

pub fn clone(&self) -> DecisionTreeRegressorParameters

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for DecisionTreeRegressorParameters

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

Formats the value using the given formatter.

impl Default for DecisionTreeRegressorParameters

pub fn default() -> Self

Returns the "default value" for a type.

impl<'de> Deserialize<'de> for DecisionTreeRegressorParameters

pub fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

impl Serialize for DecisionTreeRegressorParameters

pub fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<T: RealNumber, M: Matrix<T>> SupervisedEstimator<M, <M as BaseMatrix<T>>::RowVector, DecisionTreeRegressorParameters> for DecisionTreeRegressor<T>

pub fn fit(
    x: &M,
    y: &M::RowVector,
    parameters: DecisionTreeRegressorParameters
) -> Result<Self, Failed>

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