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.

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.

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

Class CrossValidation