getMetrics: Performance Measures

In khadijaaziz/icardaFIGSr: Subsetting using Focused Identification of the Germplasm Strategy (FIGS)

Description

this function allows to obtain performance measures from Confusion Matrix, it returns a data frame containing performance measures from the confusion matrix given by the caret package.

Usage

getMetrics(y, yhat, classtype)

Arguments

y	expression. The class variable.
yhat	expression. The vector of predicted values.
classtype	character or numeric. The number of levels in y.

Details

getMetrics works with target variables that have two, three, four, six or eight classes.

The function relies on the caret package to obtain the confusion matrix from which performance measures are extracted. It can be run for several algorithms, and the results combined into one data frame for easier comparison (see section 'Examples').

Predictions have to be obtained beforehand and used as input for yhat. The predict.train function in caret should be run without argument type when obtaining the predictions.

Value

Outputs an object with performance measures calculated from the confusion matrix given by the caret package. A data frame is the resulting output with the first column giving the name of the performance measure, and the second column giving the corresponding value.

Author(s)

Zakaria Kehel, Bancy Ngatia, Khadija Aziz

See Also

confusionMatrix

Examples

if(interactive()){
# Obtain predictions from previous models
 
data(septoriaDurumWC)  
split.data <- splitData(septoriaDurumWC, seed = 1234, y = "ST_S", p = 0.7)
data.train <- split.data$trainset
data.test <- split.data$testset

knn.mod <- tuneTrain(data = septoriaDurumWC,y = 'ST_S',method = 'knn',positive = 'R')
nnet.mod <- tuneTrain(data = septoriaDurumWC,y = 'ST_S',method = 'nnet',positive = 'R') 

pred.knn <- predict(knn.mod$Model, newdata = data.test[ , -1])
pred.nnet <- predict(nnet.mod$Model, newdata = data.test[ , -1])

metrics.knn <- getMetrics(y = data.test$ST_S, yhat = pred.knn, classtype = 2)
metrics.nnet <- getMetrics(y = data.test$ST_S, yhat = pred.nnet, classtype = 2)
}

khadijaaziz/icardaFIGSr documentation built on Dec. 21, 2021, 6:38 a.m.