smile.validation

Class CrossValidation

java.lang.Object

smile.validation.CrossValidation

public class CrossValidation
extends java.lang.Object

Cross-validation is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds.

Field Summary

Fields

Modifier and Type	Field and Description
int	k The number of rounds of cross validation.
int[][]	test The index of testing instances.
int[][]	train The index of training instances.

Constructor Summary

Constructors

Constructor and Description
CrossValidation(int n, int k) Constructor.
CrossValidation(int n, int k, boolean permutate) Constructor.

Method Summary

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

k

public final int k

The number of rounds of cross validation.

train

public final int[][] train

The index of training instances.

test

public final int[][] test

The index of testing instances.

Constructor Detail

CrossValidation

public CrossValidation(int n,
                       int k)

Constructor.

Parameters:

n - the number of samples.

k - the number of rounds of cross validation.

CrossValidation

public CrossValidation(int n,
                       int k,
                       boolean permutate)

Constructor.

Parameters:

n - the number of samples.

k - the number of rounds of cross validation.

permutate - determiner of index permutation