smile.base.cart

Enum Loss.Type

java.lang.Object

java.lang.Enum<Loss.Type>

smile.base.cart.Loss.Type

All Implemented Interfaces:

java.io.Serializable, java.lang.Comparable<Loss.Type>

Enclosing interface:

Loss

public static enum Loss.Type
extends java.lang.Enum<Loss.Type>

The type of loss.

Enum Constant Summary

Enum Constants

Enum Constant and Description
Huber Huber loss function for M-regression, which attempts resistance to long-tailed error distributions and outliers while maintaining high efficency for normally distributed errors.
LeastAbsoluteDeviation Least absolute deviation regression.
LeastSquares Least squares regression.
Quantile Quantile regression.

Method Summary

Modifier and Type	Method and Description
static Loss.Type	valueOf(java.lang.String name) Returns the enum constant of this type with the specified name.
static Loss.Type[]	values() Returns an array containing the constants of this enum type, in the order they are declared.

Methods inherited from class java.lang.Enum

clone, compareTo, equals, finalize, getDeclaringClass, hashCode, name, ordinal, toString, valueOf

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Enum Constant Detail

LeastSquares

public static final Loss.Type LeastSquares

Least squares regression. Least-squares is highly efficient for normally distributed errors but is prone to long tails and outliers.

Quantile

public static final Loss.Type Quantile

Quantile regression. The gradient tree boosting based on this loss function is highly robust. The trees use only order information on the input variables and the pseudo-response has only two values {-1, +1}. The line searches (terminal node values) use only specified quantile ratio.

LeastAbsoluteDeviation

public static final Loss.Type LeastAbsoluteDeviation

Least absolute deviation regression. The gradient tree boosting based on this loss function is highly robust. The trees use only order information on the input variables and the pseudo-response has only two values {-1, +1}. The line searches (terminal node values) use only medians. This is a special case of quantile regression of q = 0.5.

Huber

public static final Loss.Type Huber

Huber loss function for M-regression, which attempts resistance to long-tailed error distributions and outliers while maintaining high efficency for normally distributed errors.

Method Detail

values

public static Loss.Type[] values()

Returns an array containing the constants of this enum type, in the order they are declared. This method may be used to iterate over the constants as follows:

for (Loss.Type c : Loss.Type.values())
    System.out.println(c);

Returns:

an array containing the constants of this enum type, in the order they are declared

valueOf

public static Loss.Type valueOf(java.lang.String name)

Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)

Parameters:

name - the name of the enum constant to be returned.

Returns:

the enum constant with the specified name

Throws:

java.lang.IllegalArgumentException - if this enum type has no constant with the specified name

java.lang.NullPointerException - if the argument is null