These test classification confusion matrix, error report, and other metrics are the result of a technique used to classify customers with low risk who apply for loans at a credit union. Low risk is class 1, and high risk is class 0.What do the KNN classification evaluation metrics suggest about the model? Thank you!

Confusion Matrix Actual\\Predicted . 0 0 16 15 22 31 Error Report Class # Cases # Errors %% Error 31 15 48.38709677 53 22 41.50943396 Overall 84 37 44.04761905 Metrics Metric Value Accuracy (#correct) 47 Accuracy (%correct) 55.95238095 Specificity 0.516129032 Sensitivity (Recall) 0.58490566 Precision 0.673913043 F1 store 0.626262626 Success Class Success Probability 0.5 These test classification confusion matrix, error report, and other metrics are the result of a technique used to classify customers with low risk who apply for loans at a Credit Union. Low risk is class 1, and high risk is class 0. What do the KNN classification evaluation metrics suggest about the model? Select one: O a. All the given answers are true O b. The model is better at classifying customers with high risk. O c. The model has low efficacy (effectiveness), in fact, it is just somewhat better than random prediction. O d. The model is better at classifying customers with high risk, and has low efficacy (effectiveness). In fact, it is just somewhat better than random prediction. e. The model has a near-perfect Precision metric