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)