1. Binomial logistic regression predicts category using one or more _____ variables.

✔ independent
❌ categorical
❌ dependent
❌ continuous

---

2. What is the logit formula?

✔ Logarithm of p divided by 1 minus p
❌ Log of 1/p − 1
❌ Log of p+1/p
❌ Log of (1+p)/p

---

3. MLE is a technique for _____ beta parameters.

✔ estimating
❌ limiting
❌ eliminating
❌ duplicating

---

4. Linearity assumption: linear relationship with logit of probability that:

✔ Y = 1
❌ X = Y
❌ Y = 0
❌ X = 1

---

5. Confusion matrix predicts labels for a _____ variable.

✔ categorical
❌ confidence
❌ continuous
❌ correlated

---

6. Precision formula:

Precision = TP / (TP + FP) = 116 / (116 + 3)
✔ 116 / (116 + 3)
❌ 91 / (116 + 3)
❌ (91 + 3) / 116
❌ 116 / (3 + 2)

---

7. Accuracy formula:

Accuracy = (TP + TN) / Total = (99 + 91) / 248
✔ (99 + 91) / 248
❌ (248 − 99) / 91
❌ 99 / (248 − 91)
❌ 248 / (99 + 91)

---

8. Recall formula:

Recall = TP / (TP + FN) = 99 / (99 + 4)
✔ 99 / (99 + 4)
❌ 80 / (80 + 7)
❌ (84 + 4) / 80
❌ (99 − 7) / (80 − 4)

---

9. FPR vs TPR graph =

✔ ROC curve
❌ Log-loss curve
❌ Entropy curve
❌ Confusion matrix

---

10. Aggregate measure over all thresholds =

✔ AUC (Area under ROC)
❌ ROC
❌ Balanced accuracy
❌ Confusion score

---

🧾 Summary Table of All Answers

---