LossQuantile: Quantile loss for regression tasks.

In schalkdaniel/compboost: Efficient Component-Wise Boosting Implementation

Quantile loss for regression tasks.

Description

This loss can be used for regression with y \in \mathrm{R}.

Arguments

offset	(numeric(1) | matrix()) Numerical value or matrix to set a custom offset. If used, this value is returned instead of the loss optimal initialization.
quantile	(numeric(1)) Numerical value between 0 and 1 that defines the quantile that is modeled.

Format

S4 object.

Details

Loss Function:

L(y, f(x)) = h| y - f(x)|

Gradient:

\frac{\delta}{\delta f(x)}\ L(y, f(x)) = -h\mathrm{sign}( y - f(x))

Initialization:

\hat{f}^{[0]}(x) = \mathrm{arg~min}_{c\in R}\ \frac{1}{n}\sum\limits_{i=1}^n L(y^{(i)}, c) = \mathrm{quantile}(y, q)

Usage

LossAbsolute$new()
LossAbsolute$new(quantile)
LossAbsolute$new(offset, quantile)

Inherited methods from Loss

• ⁠$loss()⁠: ⁠matrix(), matrix() -> matrix()⁠

• ⁠$gradient()⁠: ⁠matrix(), matrix() -> matrix()⁠

• ⁠$constInit()⁠: matrix() -> matrix()

• ⁠$calculatePseudoResiduals()⁠: ⁠matrix(), matrix() -> matrix()⁠

• ⁠$getLossType()⁠: ⁠() -> character(1)⁠

Examples

# Create new loss object:
quadratic_loss = LossQuadratic$new()
quadratic_loss

schalkdaniel/compboost documentation built on April 15, 2023, 9:03 p.m.