Model training consists of three major tasks: method selection, performance evaluation, and model tuning.
Method selection: This decision is based on the following factors:
Performance evaluation: Commonly used techniques are:
The following metrics are used to evaluate a confusion matrix:
Precision (P) = TP/(TP + FP)
Recall (R) = TP/(TP + FN)
Accuracy = (TP + TN)/(TP + FP + TN + FN)
F1 score = (2 * P * R)/(P + R)
The higher the accuracy and the F1 score, the better the model performance.