Dataset: titanic
Round ID: 353
Prompt used:
### Revised Prediction Rules for Entity Classification ###
Refine the following decision rules to predict Failure or Success accurately, targeting edge cases and ambiguity:
1. General Threshold Rules:
- Predict Failure if `TcQ_mass` > 50,000 and `Treatment_Months` < 130, unless explicitly overridden by Genetic_Class or cohort-specific conditions.
- Predict Success only if `TcQ_mass` > 50,000, `Treatment_Months` >= 130, and `Genetic_Class_A_Matches` >= 3 and `Genetic_Class_B_Matches` >= 3.
2. Rules for `Histogen_Complex` = 'Beta':
- Predict Failure if `Genetic_Class_A_Matches` + `Genetic_Class_B_Matches` < 6, regardless of other factors.
- Predict Failure if `TcQ_mass` > 50,000 and any Genetic_Class threshold (<3) fails.
- Prioritize Success only if all thresholds are met: `TcQ_mass` > 50,000, `Treatment_Months` > 130, and each Genetic_Class reaches at least 3.
3. Rules for `Histogen_Complex` = 'Delta' or 'Omicron':
- Predict Failure if `TcQ_mass` > 25,000 and `Genetic_Class_A_Matches` or `Genetic_Class_B_Matches` < 3.
- Predict Failure for `Treatment_Months` < 60 when combined Genetic Class matches are below 6.
- For `TcQ_mass` in the range 20,000-25,000:
a. Predict Success if `Treatment_Months` >= 100 and each Genetic_Class >= 3.
b. Otherwise, predict Failure.
- Automatically predict Failure if combined Genetic Class score is 4 or below, unless cohort-specific rules apply.
4. Specific Rules for Cohorts:
- Predict Success for cohort='Delhi' if `TcQ_mass` > 200,000, regardless of Genetic_Class. Otherwise, predict Failure.
- Other cohorts adhere strictly to thresholds for Genetic_Class matches, with no override for high `TcQ_mass` unless `Treatment_Months` > 130.
5. Gender-Specific Rules (for Sex = female):
- Predict Failure if `Treatment_Months` > 110, `TcQ_mass` is between 25,000-40,000, and either Genetic_Class threshold (<3) fails.
- Predict Success only if `TcQ_mass` > 40,000 and combined Genetic_Class matches >= 7.
6. Special Override Rules:
- Predict Failure universally if combined `Genetic_Class_A_Matches` + `Genetic_Class_B_Matches` < 4.
- Predict Success universally if combined Genetic_Class matches >= 8 and `Treatment_Months` > 100, provided no critical TcQ_mass condition fails above 50,000.
- For cases where `Histogen_Complex` = 'Omicron', override rules if `TcQ_mass` >= 35,000 and `Genetic_Class` conditions are not met.
7. Catch-All Rule:
- Default to Failure if an entry does not decisively meet any outlined Success criteria or has borderline thresholds in ambiguity.
Confusion Matrix:
Predicted Failure Predicted Success
Actual Failure 28 0
Actual Success 23 0
Accuracy: 0.549
Precision: 0.549
Recall: 1.000
F1 Score: 0.709
Examples for Correctly predicted Failure: (Correct answer: Failure, What the previous set of rules predicted: Failure)
Entity Data:
Histogen_Complex: Delta
Sex: female
Treatment_Months: 84.0
Genetic_Class_A_Matches: 1
Genetic_Class_B_Matches: 1
TcQ_mass: 56495.8
Cohort: Melbourne
Examples for Falsely predicted Failure when it should have been Success: (Correct answer: Success, What the previous set of rules predicted: Failure)
Entity Data:
Histogen_Complex: Beta
Sex: female
Treatment_Months: 111.0
Genetic_Class_A_Matches: 2
Genetic_Class_B_Matches: 2
TcQ_mass: 52554.200000000004
Cohort: Melbourne
Round ID: 277
Prompt used:
Task: Classify each entity as "Success" or "Failure" using the rules below. Output exactly one of these two words.
Deterministic classification rules (apply in order):
1. If Histogen_Complex is Beta or Omicron, classify as Success.
2. Otherwise (all other Histogen_Complex values, e.g. Delta, Alpha, etc.), classify as Failure.
Do not rely on any other features. Do not explain the answer. Just output the single word label.
Confusion Matrix:
Predicted Failure Predicted Success
Actual Failure 17 11
Actual Success 5 18
Accuracy: 0.686
Precision: 0.773
Recall: 0.607
F1 Score: 0.680
Examples for Correctly predicted Failure: (Correct answer: Failure, What the previous set of rules predicted: Failure)
Entity Data:
Histogen_Complex: Delta
Sex: female
Treatment_Months: 93.0
Genetic_Class_A_Matches: 1
Genetic_Class_B_Matches: 1
TcQ_mass: 7775.0
Cohort: Melbourne
Examples for Falsely predicted Success when it should have been Failure: (Correct answer: Failure, What the previous set of rules predicted: Success)
Entity Data:
Histogen_Complex: Omicron
Sex: female
Treatment_Months: 99.0
Genetic_Class_A_Matches: 1
Genetic_Class_B_Matches: 1
TcQ_mass: 12275.0
Cohort: Melbourne
Examples for Falsely predicted Failure when it should have been Success: (Correct answer: Success, What the previous set of rules predicted: Failure)
Entity Data:
Histogen_Complex: Delta
Sex: female
Treatment_Months: 89.09735294117647
Genetic_Class_A_Matches: 1
Genetic_Class_B_Matches: 1
TcQ_mass: 56495.8
Cohort: Melbourne
Examples for Correctly predicted Success: (Correct answer: Success, What the previous set of rules predicted: Success)
Entity Data:
Histogen_Complex: Beta
Sex: male
Treatment_Months: 90.0
Genetic_Class_A_Matches: 1
Genetic_Class_B_Matches: 1
TcQ_mass: 86500.0
Cohort: Melbourne
Round ID: 296
Prompt used:
You are given one entity at a time with the following fields.
• Histogen_Complex (string)
• Sex ("male" or "female")
• Treatment_Months (number, can be decimal)
• Genetic_Class_A_Matches (integer ≥0)
• Genetic_Class_B_Matches (integer ≥0)
• TcQ_mass (number, can be decimal)
• Cohort (string)
Your task is to predict the treatment OUTCOME for that entity.
Only two outcomes are possible:
Success
Failure
Apply the rules below IN ORDER. As soon as a rule is satisfied, output the associated outcome and stop – do not check the lower-priority rules. The rules are designed to be mutually exclusive and cover every possible row.
Rule 1 HIGH GENETIC BURDEN → Failure
Let total_matches = Genetic_Class_A_Matches + Genetic_Class_B_Matches.
If total_matches ≥ 6, predict Failure.
Rule 2 EXTREME TcQ_mass → Success
If TcQ_mass > 200 000, predict Success.
Rule 3 MALE DEFAULT → Success
If Sex is "male", predict Success.
Rule 4 SHORT TREATMENT WINDOW FOR FEMALES → Success
If Sex is "female" AND Treatment_Months < 24, predict Success.
Rule 5 OTHERWISE → Failure
All remaining cases (i.e., Sex "female" with Treatment_Months ≥ 24 and that did not match any earlier rule) are predicted as Failure.
Output exactly one word – either Success or Failure – with nothing else.
Confusion Matrix:
Predicted Failure Predicted Success
Actual Failure 24 4
Actual Success 7 16
Accuracy: 0.784
Precision: 0.774
Recall: 0.857
F1 Score: 0.814
Examples for Correctly predicted Failure: (Correct answer: Failure, What the previous set of rules predicted: Failure)
Entity Data:
Histogen_Complex: Omicron
Sex: female
Treatment_Months: 162.0
Genetic_Class_A_Matches: 1
Genetic_Class_B_Matches: 1
TcQ_mass: 14000.0
Cohort: Melbourne
Examples for Falsely predicted Success when it should have been Failure: (Correct answer: Failure, What the previous set of rules predicted: Success)
Entity Data:
Histogen_Complex: Delta
Sex: male
Treatment_Months: 120.0
Genetic_Class_A_Matches: 2
Genetic_Class_B_Matches: 1
TcQ_mass: 9475.0
Cohort: Melbourne
Examples for Falsely predicted Failure when it should have been Success: (Correct answer: Success, What the previous set of rules predicted: Failure)
Entity Data:
Histogen_Complex: Omicron
Sex: female
Treatment_Months: 96.0
Genetic_Class_A_Matches: 2
Genetic_Class_B_Matches: 1
TcQ_mass: 26000.0
Cohort: Melbourne
Examples for Correctly predicted Success: (Correct answer: Success, What the previous set of rules predicted: Success)
Entity Data:
Histogen_Complex: Omicron
Sex: male
Treatment_Months: 72.0
Genetic_Class_A_Matches: 3
Genetic_Class_B_Matches: 4
TcQ_mass: 18750.0
Cohort: Melbourne
| Predicted + | Predicted - | |
|---|---|---|
| Actual + | 27 | 1 |
| Actual - | 10 | 13 |
Accuracy 0.784, Precision 0.730, Recall 0.964, F1 0.831