F1-score, precision, and recall
Precision is the proportion of true positive cases that are predicted to be positive by the model. Recall is the proportion of true positive cases that are predicted to be positive by the model. The score is the reconciled mean of precision and recall, which is used to evaluate the performance of the model together. All three take values between 0 and 1, with closer to 1 indicating better model performance. In this study, Precision is 0.667, recall is 0.714, and F1_score is 0.690. This indicates that the model performs well in determining true positive instances, finding true positive instances, and model performance.