cross_validation {abcrlda} | R Documentation |

## Cross Validation for separate sampling adjusted for cost.

### Description

This function implements Cross Validation for separate sampling adjusted for cost.

### Usage

```
cross_validation(
x,
y,
gamma = 1,
cost = c(0.5, 0.5),
nfolds = 10,
bias_correction = TRUE
)
```

### Arguments

`x` |
Input matrix or data.frame of dimension |

`y` |
A numeric vector or factor of class labels. Factor should have either two levels or be
a vector with two distinct values.
If |

`gamma` |
Regularization parameter
Formulas and derivations for parameters used in above equation can be found in the article under reference section. |

`cost` |
Parameter that controls the overall misclassification costs.
This is a vector of length 1 or 2 where the first value is If a single value is provided, it should be normalized to lie between 0 and 1 (but not including 0 or 1).
This value will be assigned to |

`nfolds` |
Number of folds to use with cross-validation. Default is 10.
In case of imbalanced data, |

`bias_correction` |
Takes in a boolean value.
If |

### Value

Returns list of parameters.

`risk_cross` |
Returns risk estimation where |

`e_0` |
Error estimate for class 0. |

`e_1` |
Error estimate for class 1. |

### Reference

Braga-Neto, Ulisses & Zollanvari, Amin & Dougherty, Edward. (2014). Cross-Validation Under Separate Sampling: Strong Bias and How to Correct It. Bioinformatics (Oxford, England). 30. 10.1093/bioinformatics/btu527. URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4296143/pdf/btu527.pdf

### See Also

Other functions in the package:
`abcrlda()`

,
`da_risk_estimator()`

,
`grid_search()`

,
`predict.abcrlda()`

,
`risk_calculate()`

### Examples

```
data(iris)
train_data <- iris[which(iris[, ncol(iris)] == "virginica" |
iris[, ncol(iris)] == "versicolor"), 1:4]
train_label <- factor(iris[which(iris[, ncol(iris)] == "virginica" |
iris[, ncol(iris)] == "versicolor"), 5])
cross_validation(train_data, train_label, gamma = 10)
```

*abcrlda*version 1.0.3 Index]