Ensemble

Dataset: timetravel_insurance

Models

• openailong
• openai35
• random

Model Narratives

openailong

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: 11.322671
	ParadoxCount: 1.2654697

openai35

Round ID: 416
Prompt used:
	Choose randomly

Confusion Matrix:
                Predicted Approved   Predicted Denied    
Actual Approved                    4                    5
Actual Denied                      4                    7

Accuracy: 0.550
Precision: 0.500
Recall: 0.444
F1 Score: 0.471

Examples for Correctly predicted Approved: (Correct answer: Approved, What the previous set of rules predicted: Approved)
  Entity Data:
	TimelineDeviation: 9.241727
	ParadoxCount: 6.9502397


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: 12.904642
	ParadoxCount: 5.0420074


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: 8.337469
	ParadoxCount: 6.267838

random

Round ID: 161
Prompt used:
	Choose randomly

Confusion Matrix:
                Predicted Approved   Predicted Denied    
Actual Approved                    6                    3
Actual Denied                      7                    4

Accuracy: 0.500
Precision: 0.462
Recall: 0.667
F1 Score: 0.545

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: 9.348428
	ParadoxCount: 1.573731


Examples for Correctly predicted Denied: (Correct answer: Denied, What the previous set of rules predicted: Denied)
  Entity Data:
	TimelineDeviation: 8.337469
	ParadoxCount: 6.267838

Ensemble Confusion Matrix

Accuracy 0.700, Precision 0.636, Recall 0.778, F1 0.700