*5.4. Model Evaluation*

In the model shown in Figure 5, the training sample data are split into a training set and a test set by a "Data set split" module, and the split ratio is shown in Table 4. The test set is evaluated by the "Model Evaluation" module in the MLS. The models are quantitatively evaluated by using model evaluation indicators, such as confusion matrix, precision, recall rate, and an *F*1 score. The specific definitions of each indicator are as follows.

Taking the binary classification problem as an example, the sample data are divided into true positive (TP), false positive (FP), true negative (TN), and false negative (FN), according to the combination of its real category and machine learning prediction category. Then, TP + FP + TN + FN = Total number of samples.
