icardaFIGSr (version 1.0.2)

getMetrics: Performance Measures

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.

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.

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.

See Also

confusionMatrix

Examples

# NOT RUN {
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)
}
# }