Dataset: timetravel_insurance
Round ID: 225
Prompt used:
You are an expert who decides whether each Entity is “Approved” or “Denied” based on these explicit rules, in the following order:
1) If TimelineDeviation < 8.0 AND ParadoxCount < 2.0, then Approve. (Covers low-deviation, very-low-paradox cases that should be approved.)
2) If TimelineDeviation < 8.0 AND ParadoxCount > 3.7, then Approve. (Expands approval for moderately low deviation but high paradox counts.)
3) If TimelineDeviation < 8.0, then Deny. (All other cases below 8.0 get denied.)
4) If ParadoxCount < 2.5 AND TimelineDeviation < 16.0, then Deny. (Keeps consistent denial of very low paradox for moderate deviations.)
5) If 9.0 <= TimelineDeviation < 10.0 AND ParadoxCount < 6.0, then Deny. (Addresses the previously false-Approved mid-paradox count in the 9–10 deviation range.)
6) If ParadoxCount > 9.3 AND TimelineDeviation < 10.0, then Deny. (Retains a high paradox cutoff but raised to 9.3 to avoid denying borderline 9.2–9.3 cases that should be approved.)
7) Otherwise, Approve.
Decision Logic:
• Evaluate each rule in the order above. The first rule that matches the Entity’s data determines the final answer.
• If no rule matches, default to “Approved.”
Output exactly one word as your final answer for each data row: either “Approved” or “Denied.”
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 9 0
Actual Denied 7 4
Accuracy: 0.650
Precision: 0.562
Recall: 1.000
F1 Score: 0.720
Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 11.653055
ParadoxCount: 6.0099745
Examples for Falsely predicted Approved when it should have been Denied: (Correct answer: Denied, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 8.813088
ParadoxCount: 5.151609
Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 9.348428
ParadoxCount: 1.573731
Round ID: 316
Prompt used:
To make predictions about whether an entity should be approved or denied, follow these clear and explicit rules based on the given features:
1. If `TimelineDeviation` is greater than 12 and `ParadoxCount` is greater than 6, then predict 'Approved'.
2. If `TimelineDeviation` is between 10 and 12:
- Predict 'Approved' if `ParadoxCount` is greater than or equal to 5.
- Predict 'Denied' if `ParadoxCount` is less than 5 and `TimelineDeviation` is less than 12.
3. If `TimelineDeviation` is less than or equal to 10:
- Predict 'Denied' if `ParadoxCount` is less than or equal to 3.
- Predict 'Approved' if `ParadoxCount` is exactly 4.
4. If `TimelineDeviation` is between 12 and 14 and `ParadoxCount` is greater than 6, then predict 'Approved'.
5. If `TimelineDeviation` is greater than 12 and `ParadoxCount` is between 5 and 6, predict 'Approved'.
6. If `TimelineDeviation` is greater than or equal to 10 but less than or equal to 12 and `ParadoxCount` is less than or equal to 6, then predict 'Denied'.
7. For all other combinations of `TimelineDeviation` and `ParadoxCount` that do not meet the criteria above, predict 'Denied'.
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 6 3
Actual Denied 1 10
Accuracy: 0.800
Precision: 0.857
Recall: 0.667
F1 Score: 0.750
Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 16.56909
ParadoxCount: 7.107604
Examples for Falsely predicted Denied when it should have been Approved: (Correct answer: Approved, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 15.171367
ParadoxCount: 3.6933415
Examples for Falsely predicted Approved when it should have been Denied: (Correct answer: Denied, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 13.023456
ParadoxCount: 6.9185414
Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 8.337469
ParadoxCount: 6.267838
Round ID: 278
Prompt used:
You are given two numeric values for an entity: “TimelineDeviation” (T) and “ParadoxCount” (P). Using the rules below in the exact order, decide whether to label the entity as “Approved” or “Denied.” Your output should be only the word “Approved” or “Denied.”
Rules:
1) If T < 8:
a) If P ≥ 8, label “Approved.”
b) Otherwise, label “Denied.”
(Rationale: Even with very low timelines, an extremely high paradox count can justify approval.)
2) Else if 8 ≤ T < 10:
a) If P ≥ 6, label “Approved.”
b) Otherwise, label “Denied.”
(Rationale: Slightly raised the paradox threshold in this range to reduce false approvals.)
3) Else if T > 16:
a) If P < 2, label “Denied.”
b) Otherwise, label “Approved.”
(Rationale: We still deny extremely high T with too few paradoxes, but approve otherwise.)
4) Else if 10 ≤ T < 12:
a) If P ≥ 4.5, label “Approved.”
b) Otherwise, label “Denied.”
(Rationale: Maintains a proven threshold in the 10–12 range.)
5) Else if 12 ≤ T < 13:
a) If P ≥ 8, label “Denied.”
(Rationale: Extremely high paradox counts in this narrow range can lead to paradoxical instability, so we deny.)
b) Else if P ≥ 4, label “Approved.”
(Rationale: This fixes previous false denials when P was around 6–7.)
c) Otherwise, label “Denied.”
(Rationale: We continue to deny lower paradox counts here.)
6) Else if 13 ≤ T ≤ 16:
a) If T ≥ 15 AND P < 3, label “Denied.”
(Rationale: Very high T near 15 with too few paradoxes is still denied.)
b) Else if P ≥ 8, label “Approved.”
(Rationale: Extremely large paradox count is approved in this range.)
c) Else if P ≥ 6, label “Denied.”
(Rationale: Moderate paradox counts at higher T caused false positives previously, so we deny these.)
d) Otherwise, label “Approved.”
(Rationale: Everything else in 13–16 is generally approved unless it meets the exceptions above.)
Remember: Apply these rules in order and output only “Approved” or “Denied.”
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 7 2
Actual Denied 2 9
Accuracy: 0.800
Precision: 0.778
Recall: 0.778
F1 Score: 0.778
Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 11.124919
ParadoxCount: 6.3591957
Examples for Falsely predicted Denied when it should have been Approved: (Correct answer: Approved, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 14.890128
ParadoxCount: 6.380288
Examples for Falsely predicted Approved when it should have been Denied: (Correct answer: Denied, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 9.459619
ParadoxCount: 6.900848
Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 11.322671
ParadoxCount: 1.2654697
| Predicted + | Predicted - | |
|---|---|---|
| Actual + | 8 | 1 |
| Actual - | 3 | 8 |
Accuracy 0.800, Precision 0.727, Recall 0.889, F1 0.800