tab2meas calculate the measures for a multiclass classification model.
pred2meas calculates the measures for a regression model.
1 2 3 4 5 6 7 8 9 10 11
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
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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.