com.imsl.datamining.decisionTree

Enum DecisionTreeInfoGain.GainCriteria

java.lang.Object

java.lang.Enum<DecisionTreeInfoGain.GainCriteria>

com.imsl.datamining.decisionTree.DecisionTreeInfoGain.GainCriteria

All Implemented Interfaces:

Serializable, Comparable<DecisionTreeInfoGain.GainCriteria>

Enclosing class:

DecisionTreeInfoGain

public static enum DecisionTreeInfoGain.GainCriteria
extends Enum<DecisionTreeInfoGain.GainCriteria>

Specifies which information gain criteria to use in determining the best split at each node.

Enum Constant Summary

Enum Constants

Enum Constant and Description
DEVIANCE A measure of the quality of fit.
GINI_INDEX A measure of statistical dispersion.
SHANNON_ENTROPY A measure of randomness or uncertainty.

Method Summary

Modifier and Type	Method and Description
static DecisionTreeInfoGain.GainCriteria	valueOf(String name) Returns the enum constant of this type with the specified name.
static DecisionTreeInfoGain.GainCriteria[]	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

SHANNON_ENTROPY

public static final DecisionTreeInfoGain.GainCriteria SHANNON_ENTROPY

A measure of randomness or uncertainty.

For a categorical variable having $C$ distinct values over a data set $S$, the Shannon Entropy is defined as $$\sum_{i=1}^{C}p_i\log(p_i)$$ where $$p_i = Pr(Y=i)$$ and where $$p_i \log(p_i) := 0$$ if $p_i=0$.

GINI_INDEX

public static final DecisionTreeInfoGain.GainCriteria GINI_INDEX

A measure of statistical dispersion.

For a categorical variable having $C$ distinct values over a data set $S$, the Gini index is defined as $$I(S)=\sum_{\begin{array}{c}i,j=1\\i\ne j\end{array}}^C p(i|S)=1-\sum^C_{i=1}p^2(i|S)$$ where $p(i|S)$ denotes the probability that the variable is equal to the state $i$ on the data set, $S$.

DEVIANCE

public static final DecisionTreeInfoGain.GainCriteria DEVIANCE

A measure of the quality of fit.

For a categorical variable having $C$ distinct values over a data set $S$, the Deviance measure is $$\sum_{i=1}^{C}n_i\log(p_i)$$ where $p_i = Pr(Y=i)$ and $n_i$ is the number of cases with $Y=i$ on the node.

Method Detail

values

public static DecisionTreeInfoGain.GainCriteria[] 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 (DecisionTreeInfoGain.GainCriteria c : DecisionTreeInfoGain.GainCriteria.values())
    System.out.println(c);

Returns:

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

valueOf

public static DecisionTreeInfoGain.GainCriteria valueOf(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:

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

NullPointerException - if the argument is null