Performance measures for regression and classification models
cat2meas(yobs, ypred, measure = "accuracy", cost = rep(1, nlevels(yobs))) tab2meas(tab, measure = "accuracy", cost = rep(1, nrow(tab))) pred.MSE(yobs, ypred) pred.RMSE(yobs, ypred) pred.MAE(yobs, ypred) pred2meas(yobs, ypred, measure = "RMSE")
yobs |
A vector of the labels, true class or observed response. Can be |
ypred |
A vector of the predicted labels, predicted class or predicted response. Can be |
measure |
Type of measure, see |
cost |
Cost value by class (only for input factors). |
tab |
Confusion matrix (Contingency table: observed class by rows, predicted class by columns). |
cat2meas compute tab=table(yobs,ypred) and calls tab2meas function.
tab2meas function computes the following measures (see measure argument) for a binary classification model:
accuracy the accuracy classification score
recall, sensitivity,TPrate R=TP/(TP+FN)
precision P=TP/(TP+FP)
specificity,TNrate TN/(TN+FP)
FPrate FP/(TN+FP)
FNrate FN/(TP+FN)
Fmeasure 2/(1/R+1/P)
Gmean sqrt(R*TN/(TN+FP))
kappa the kappa index
cost sum(diag(tab)/rowSums(tab)*cost)/sum(cost)
pred2meas function computes the following measures of error, usign the measure argument, for observed and predicted vectors:
MSE Mean squared error, ∑ (ypred- yobs)^2 /n
RMSE Root mean squared error √(∑ (ypred- yobs)^2 /n )
MAE Mean Absolute Error, ∑ |yobs - ypred| /n
Other performance:
weights4class()
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.