tab2meas calculate the measures for a multiclass classification model.
pred2meas calculates the measures for a regression model.
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")
A vector of the labels, true class or observed response. Can be
A vector of the predicted labels, predicted class or predicted response. Can be
Type of measure, see
Cost value by class (only for input factors).
Confusion matrix (Contingency table: observed class by rows, predicted class by columns).
cat2meas compute tab=table(yobs,ypred) and calls
tab2meas function computes the following measures (see
measure argument) for a binary classification model:
accuracy the accuracy classification score
kappa the kappa index
IOU TP/(TP+FN+FP) mean of Intersection over Union
IOU4class TP/(TP+FN+FP) Intersection over Union by level
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
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