Dataset: timetravel_insurance
Round ID: 8
Prompt used:
Classification Decision Rules:
Metric 1: TimelineDeviation Scoring
- If TimelineDeviation > 15: Add 3 points to APPROVAL score
- If 12 < TimelineDeviation ≤ 15: Add 2.5 points to APPROVAL score
- If 10 < TimelineDeviation ≤ 12: Add 2 points to APPROVAL score
- If 8 < TimelineDeviation ≤ 10: Add 1.5 points to APPROVAL score
- If 7 < TimelineDeviation ≤ 8: Add 1 point to APPROVAL score
- If 5 ≤ TimelineDeviation < 7:
* Interpolate 1-2 points to DENIAL score
* If ParadoxCount is also low (≤ 2), add an additional 0.5-1 point to DENIAL score
- If TimelineDeviation < 5: Add 2-3 points to DENIAL score
Metric 2: ParadoxCount Scoring
- If ParadoxCount > 6: Add 3 points to APPROVAL score
- If 4 < ParadoxCount ≤ 6: Add 2.5 points to APPROVAL score
- If 3 < ParadoxCount ≤ 4: Add 2 points to APPROVAL score
- If 2 < ParadoxCount ≤ 3: Add 1.5 points to APPROVAL score
- If 1 < ParadoxCount ≤ 2: Add 1 point to DENIAL score
- If ParadoxCount ≤ 1:
* Add 2-3 points to DENIAL score
* Implement an aggressive penalty if TimelineDeviation is also low
Critical Interaction and Balance Rules:
- Introduce a "Metric Balance Coefficient":
* Calculate the ratio between TimelineDeviation and ParadoxCount
* If ratio indicates high imbalance (e.g., one metric is > 3x the other):
- Add 0.5-1 point penalty to the score with lower value
- Reduce potential score for the overcompensating metric
Negative ParadoxCount Special Handling:
- If ParadoxCount < 0:
* If absolute(ParadoxCount) ≤ 1: Add 2.5-3 points to DENIAL score
* If absolute(ParadoxCount) > 1 AND ≤ 2:
- Add 3.5 points to DENIAL score
- Reduce potential APPROVAL score by 1.5 points
* If absolute(ParadoxCount) > 2:
- Add 4 points to DENIAL score
- Completely nullify potential APPROVAL score
Compensatory and Edge Case Mechanisms:
- For TimelineDeviation ≤ 8 AND ParadoxCount ≤ 3:
* Strongly penalize potential APPROVAL
* Add 1-1.5 points to DENIAL score
- For TimelineDeviation > 10 AND ParadoxCount < 3:
* Add 0.5 bonus points to APPROVAL score
- For TimelineDeviation < 7 AND ParadoxCount > 5:
* Add 0.5 bonus points to DENIAL score
Final Classification:
- If APPROVAL score ≥ 4: Classify as APPROVED
- If DENIAL score ≥ 4: Classify as DENIED
- Borderline Zone (APPROVAL score 3.5-4, DENIAL score 3.5-4):
* Use weighted interpolation with stricter lean towards DENIAL
* Strongly favor DENIAL if TimelineDeviation is low
- If scores are exactly tied or within 0.5 points: Require additional review
Tiebreaker Criteria:
- Prioritize interpolated scoring
- Give more weight to low or negative metric values
- Slight preference for DENIAL in ambiguous scenarios, especially with low metrics
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 8 1
Actual Denied 4 7
Accuracy: 0.750
Precision: 0.667
Recall: 0.889
F1 Score: 0.762
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 Denied when it should have been Approved: (Correct answer: Approved, What the previous set of rules predicted: Denied)
Entity Data:
TimelineDeviation: 11.072363
ParadoxCount: 3.584661
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: 8.813088
ParadoxCount: 5.151609
Round ID: 209
Prompt used:
To determine if an entity should be "Approved" or "Denied", use the following rules:
1. If TimelineDeviation is greater than 12 and ParadoxCount is less than 5, predict "Approved".
2. If TimelineDeviation is less than or equal to 12 or ParadoxCount is 5 or more, predict "Denied".
Use these rules strictly to make your prediction for each entry.
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 1 8
Actual Denied 1 10
Accuracy: 0.550
Precision: 0.500
Recall: 0.111
F1 Score: 0.182
Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 15.171367
ParadoxCount: 3.6933415
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: 11.124919
ParadoxCount: 6.3591957
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: 11.322671
ParadoxCount: 1.2654697
Round ID: 261
Prompt used:
To determine whether an entity should be approved or denied, apply the following refined rules based on the entity data:
1. If TimelineDeviation > 12 and ParadoxCount > 4.5, then Predict: Approved.
2. If TimelineDeviation <= 10 and ParadoxCount <= 3, then Predict: Denied.
3. If TimelineDeviation is between 10 and 12, then:
a. If ParadoxCount > 5, Predict: Approved.
b. Otherwise, Predict: Denied.
4. NEW RULE: If TimelineDeviation <= 10 and ParadoxCount > 6, then Predict: Approved.
5. REFINED RULE: If TimelineDeviation > 10 and ParadoxCount > 7, then Predict: Denied.
This prompt incorporates additional conditions to refine decision-making and aims to reduce both false positives and false negatives by addressing identified pattern mismatches.
Confusion Matrix:
Predicted Approved Predicted Denied
Actual Approved 6 3
Actual Denied 2 9
Accuracy: 0.750
Precision: 0.750
Recall: 0.667
F1 Score: 0.706
Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
Entity Data:
TimelineDeviation: 12.904642
ParadoxCount: 5.0420074
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 + | 7 | 2 |
| Actual - | 2 | 9 |
Accuracy 0.800, Precision 0.778, Recall 0.778, F1 0.778