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: 10.313137 ParadoxCount: 6.5179386 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: 10.1948805 ParadoxCount: 3.5392668 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: 228 Prompt used: Classification Rules: Approve an entity if: 1. TimelineDeviation > 9 AND 2. (ParadoxCount > 4 OR (TimelineDeviation > 14 AND ParadoxCount > 2)) Deny an entity if: 1. TimelineDeviation < 8 OR 2. ParadoxCount < 3 OR 3. (TimelineDeviation < 12 AND ParadoxCount < 5) Special Considerations: - Entities with TimelineDeviation > 14 should be given more lenient scrutiny - Negative ParadoxCount values automatically trigger denial - Borderline cases between 8-12 TimelineDeviation require careful individual assessment Rationale: - Lowered initial TimelineDeviation threshold to 9 - Added flexibility for high TimelineDeviation entities - Introduced more nuanced ParadoxCount criteria - Provided clear rules for edge cases Confusion Matrix: Predicted Approved Predicted Denied Actual Approved 8 1 Actual Denied 2 9 Accuracy: 0.850 Precision: 0.800 Recall: 0.889 F1 Score: 0.842 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: 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: 8.337469 ParadoxCount: 6.267838
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: 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: 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: 10.494729 ParadoxCount: 1.0958244
Predicted + | Predicted - | |
---|---|---|
Actual + | 8 | 1 |
Actual - | 2 | 9 |
Accuracy 0.850, Precision 0.800, Recall 0.889, F1 0.842