Figure 7. Confusion matrix plot of 5-fold cross-validation
results. The dots in the graph are coloured according to the
classification results, with blue and red symbols being correct and
incorrect classifications, respectively. Sample size for each
observation and prediction combination is provided.
2.7 Classification result
check
Using the predictions from the behaviour classification model, we can
now return to the original ACC data to evaluate which ACC signals lead
to correct and incorrect classifications using function
plot_confusion_matrix. This function basically uses the same digraph
with near identical look to function plot_acc used earlier. The only
deviation is that all correct and incorrect predictions (identified
using the data frame from function plot_confusion_matrix) are now
identified with a solid and a dotted line, respectively, and annotated
with labels for the observed and predicted behaviours at the bottom and
top of the graph respectively (Fig. 8).