Enum Objective 

pub enum Objective {
    MeanSquaredError,
}

Defines the objective function to be optimized. The objective function provides the loss, gradient (first derivative), and hessian (second derivative) required for the XGBoost algorithm.

Variants

MeanSquaredError

The objective for regression tasks using Mean Squared Error. Loss: 0.5 * (y_true - y_pred)^2

Implementations

impl Objective

pub fn loss_function<TY: Number, Y: Array1<TY>>( &self, y_true: &Y, y_pred: &Vec<f64>, ) -> f64

Calculates the loss for each sample given the true and predicted values.

Arguments

• y_true - A vector of the true target values.
• y_pred - A vector of the predicted values.

Returns

The mean of the calculated loss values.

pub fn gradient<TY: Number, Y: Array1<TY>>( &self, y_true: &Y, y_pred: &Vec<f64>, ) -> Vec<f64>

Calculates the gradient (first derivative) of the loss function.

Arguments

• y_true - A vector of the true target values.
• y_pred - A vector of the predicted values.

Returns

A vector of gradients for each sample.

pub fn hessian<TY: Number, Y: Array1<TY>>( &self, y_true: &Y, y_pred: &[f64], ) -> Vec<f64>

Calculates the hessian (second derivative) of the loss function.

Arguments

• y_true - A vector of the true target values.
• y_pred - A vector of the predicted values.

Returns

A vector of hessians for each sample.

Trait Implementations

impl Clone for Objective

fn clone(&self) -> Objective

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Objective

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

Formats the value using the given formatter.