task stringclasses 40
values | chosen stringclasses 187
values | rejected stringclasses 199
values | chosen_distribution stringclasses 126
values | rejected_distribution stringclasses 146
values | bt_probs float64 0.5 1 | raw_decisions stringclasses 213
values | task_category stringclasses 8
values | is_original_task int64 0 0 | prompt stringclasses 40
values | __index_level_0__ int64 7.5k 9.5k | response stringlengths 49 3.49k |
|---|---|---|---|---|---|---|---|---|---|---|---|
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] * 3 + feats[8] * 2 + (1 - feats[36]) * 4)
``` | ```python
state + state * (feats[7] and feats[36]) + state * (feats[8] and feats[36]) + state * (feats[7] and feats[37]) + state * (feats[8] and feats[37])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.50%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.93%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.83%
Enrollment delivery status (1-1): ... | 0.9 | ['2', '2', '2', '2', '2', '2', '2', '2', '2', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,500 | ```python
state * (feats[7] + 2 * feats[8] + 3 * feats[16] + 4 * feats[36]) + 1
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] * 3 + feats[8] * 2 + (1 - feats[36]) * 4)
``` | ```python
state + state * (feats[7] or feats[8]) * (feats[36] or feats[37]) + 0.5*state * (feats[7] or feats[8])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.50%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.93%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.81%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.22%
Enrollment delivery status (1-1): ... | 0.9 | ['2', '2', '1', '2', '2', '2', '2', '2', '2', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,501 | ```python
state + 2 * state * (feats[7] or feats[8]) * (feats[36] or feats[37])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (1 + feats[7] * 2 + feats[8] * 2) * (1 + feats[36] * 3 + feats[37] * 2)
``` | ```python
state + 2 * state * (feats[7] or feats[8]) - (1 - feats[36])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.30%
Enrollment gestational age (11-20): 17.61%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.91%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.54%
Enrollment gestational age (11-20): 17.55%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.48%
Enrollment delivery status (1-1): ... | 0.8 | ['1', '1', '1', '1', '2', '1', '1', '1', '2', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,502 | ```python
state + 2 * (1 - feats[7]) * (feats[36] + feats[37])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + state * (feats[7] and feats[36]) + 0.5 * state * (feats[8] and feats[36])
``` | ```python
state * ( (feats[7] or feats[8]) and feats[36]) + state
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.62%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.13%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.67%
Enrollment gestational age (11-20): 17.54%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.14%
Enrollment delivery status (1-1): ... | 1 | ['2', '2', '2', '2', '2', '2', '2', '2', '2', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,503 | ```python
state + 3 * feats[7] * feats[36] + 2 * feats[7] * feats[37]
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] or feats[8]) * (1 - feats[36])
``` | ```python
state * (feats[7] + 2*feats[36])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.01%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.15%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.56%
Enrollment gestational age (11-20): 17.28%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.50%
Enrollment delivery status (1-1): ... | 0.6 | ['2', '2', '1', '2', '1', '2', '1', '1', '2', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,504 | ```python
(feats[7] * 5 + feats[8] * 3) * (feats[36] or 3 * feats[37])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] * 3 + feats[8] * 2 + (1 - feats[36]))
``` | ```python
state * (feats[7] or feats[8]) * (1 + 2 * (feats[36] or feats[37]))
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.25%
Enrollment gestational age (11-20): 17.47%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.25%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 29.08%
Enrollment gestational age (11-20): 17.40%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.96%
Enrollment delivery status (1-1): ... | 0.6 | ['2', '1', '2', '1', '1', '2', '1', '1', '2', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,505 | ```python
state * (feats[7] * 5 + feats[8] * 3 + feats[36] * 10 + feats[37] * 5)
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * ( (feats[7] or feats[8]) and (feats[36] or feats[37]) ) + state
``` | ```python
state * (feats[7] + feats[8]) * (1 - (feats[36] + feats[37]))
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.83%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.34%
Enrollment gestational age (11-20): 17.48%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.31%
Enrollment delivery status (1-1): ... | 1 | ['1', '1', '1', '1', '1', '1', '1', '1', '1', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,506 | ```python
(state + 1) * (feats[7] * 2 + feats[8] * 1 + feats[36] * 3)
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + state * ((feats[7] or feats[8]) and (feats[36] or feats[37])) + 0.1
``` | ```python
state * (feats[7] or (2 * feats[36]))
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.83%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.84%
Enrollment gestational age (11-20): 17.25%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.55%
Enrollment delivery status (1-1): ... | 0.6 | ['2', '1', '1', '2', '1', '1', '2', '1', '1', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,507 | ```python
state * (feats[7] * 2 + feats[36] * 3) + 1 # Adjust the coefficients and add a small value to ensure reward is always positive
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + 2 * state * (feats[7] or feats[8]) - (1 - feats[36])
``` | ```python
state + state * (feats[7] or feats[8]) * (feats[36] or feats[37])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.54%
Enrollment gestational age (11-20): 17.55%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.48%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.83%
Enrollment delivery status (1-1): ... | 0.7 | ['2', '2', '2', '2', '2', '1', '2', '1', '1', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,508 | ```python
state + 2 * state * (feats[7] and 3 * feats[5] and 2 * feats[36])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (1 + feats[7] * 2 + feats[8] * 2) * (1 + feats[36] * 3 + feats[37] * 2)
``` | ```python
state * (feats[7] or (2 * feats[36]))
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.30%
Enrollment gestational age (11-20): 17.61%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.91%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.84%
Enrollment gestational age (11-20): 17.25%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.55%
Enrollment delivery status (1-1): ... | 1 | ['1', '1', '1', '1', '1', '1', '1', '1', '1', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,509 | ```python
state + 3 * (feats[7] * feats[36] + feats[8] * feats[36] + feats[7] * feats[37] + feats[8] * feats[37])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] or feats[8]) * (1 - feats[36])
``` | ```python
state * (feats[7] or (2 * feats[36]))
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.01%
Enrollment gestational age (11-20): 17.76%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.15%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.84%
Enrollment gestational age (11-20): 17.25%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.55%
Enrollment delivery status (1-1): ... | 0.5 | ['2', '1', '2', '1', '1', '2', '1', '2', '1', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,510 | ```python
state * (feats[7] * 3 + feats[8] * 2 + feats[36] * 4)
```
or
```python
state + state * (feats[7] * 3 + feats[8] * 2 + feats[36] * 4)
```
or
```python
state + 2 * state * (feats[7] * 3 + feats[8] * 2 + feats[36] * 4)
```
or
```python
state + 3 * state * (feats[7] * 3 + feats[8] * 2 + feats[36] * 4)
... |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + state * ((feats[7] or feats[8]) and (feats[36] or feats[37])) + 0.1
``` | ```python
state * (feats[7] or feats[8]) / (1 + feats[36] + feats[37])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.83%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.63%
Enrollment gestational age (11-20): 17.73%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.47%
Enrollment delivery status (1-1): ... | 0.7 | ['2', '1', '1', '1', '1', '1', '1', '2', '2', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,511 | ```python
state * (feats[7] * 3 + feats[8] * 2 + feats[36] * 5 + feats[37] * 3)
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] or (2 * feats[36]))
``` | ```python
state + state * (feats[7] and feats[36]) + state * (feats[8] and feats[36]) + state * (feats[7] and feats[37]) + state * (feats[8] and feats[37])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.84%
Enrollment gestational age (11-20): 17.25%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.55%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.83%
Enrollment delivery status (1-1): ... | 0.6 | ['2', '2', '2', '2', '1', '2', '1', '1', '2', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,512 | ```python
state + 3 * (feats[6] and feats[36]) + 2 * (feats[5] and feats[16]) + 1 * (feats[5] and feats[36])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + state * (feats[7] or feats[8]) * (feats[36] or feats[37])
``` | ```python
state * ( (feats[7] or feats[8]) and feats[36]) + state
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.83%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.67%
Enrollment gestational age (11-20): 17.54%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.14%
Enrollment delivery status (1-1): ... | 0.9 | ['2', '2', '2', '2', '2', '1', '2', '2', '2', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,513 | ```python
state * (feats[7] * 3 + feats[8] * 2 + feats[36] * 4)
```
or
```python
state + 2 * state * (feats[7] + feats[8]) + 3 * state * feats[36]
```
or
```python
state + state * (feats[7] * 3 + feats[8] * 2 + feats[36] * 4 + feats[16] * 2 + feats[17] * 3)
```
In these example, feats[7] and feats[8] represent... |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] + 2 * feats[36])
``` | ```python
state + state * ((feats[7] or feats[8]) and feats[36])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.56%
Enrollment gestational age (11-20): 17.28%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.50%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.67%
Enrollment gestational age (11-20): 17.54%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.14%
Enrollment delivery status (1-1): ... | 0.9 | ['2', '2', '2', '2', '2', '1', '2', '2', '2', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,514 | ```python
state + 5 * feats[7] * feats[36] + 2 * feats[7] * feats[37]
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + state * (feats[7] or feats[8]) * (feats[36] or feats[37]) + 0.5*state * (feats[7] or feats[8])
``` | ```python
state * (feats[7] + feats[8]) * (1 - (feats[36] + feats[37]))
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.81%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.22%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 27.34%
Enrollment gestational age (11-20): 17.48%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.31%
Enrollment delivery status (1-1): ... | 0.5 | ['1', '2', '2', '2', '1', '1', '1', '2', '1', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,515 | ```python
(state + 1) * (feats[6] + 3 * feats[16] + 2 * feats[36])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + 2 * state * (feats[7] or feats[8]) * (1 - feats[36])
``` | ```python
state + state * (feats[7] or feats[8]) * (feats[36] or feats[37]) + 0.5*state * (feats[7] or feats[8])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.10%
Enrollment gestational age (11-20): 17.70%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.17%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.81%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.22%
Enrollment delivery status (1-1): ... | 0.6 | ['2', '1', '2', '1', '2', '2', '1', '2', '2', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,516 | ```python
state * (feats[7] * 2 + feats[36] * 3) + 5
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] * 3 + feats[8] * 2 + (1 - feats[36]))
``` | ```python
state + state * ((feats[7] or feats[8]) and feats[36])
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.25%
Enrollment gestational age (11-20): 17.47%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.25%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.67%
Enrollment gestational age (11-20): 17.54%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.14%
Enrollment delivery status (1-1): ... | 0.7 | ['1', '1', '2', '2', '2', '2', '2', '2', '2', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,517 | ```python
state * (3 * feats[7] * (1 + 2 * (1 - feats[36])) + 2 * feats[8] * (1 + feats[37])) + 5 * (feats[7] + feats[8]) * (feats[16] + 2 * feats[17])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + state * ( (feats[7] or feats[8]) and feats[36] )
``` | ```python
state * (feats[7] or feats[8]) * (feats[36] or feats[37]) + state
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.67%
Enrollment gestational age (11-20): 17.54%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.14%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.79%
Enrollment gestational age (11-20): 17.72%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 84.83%
Enrollment delivery status (1-1): ... | 0.9 | ['1', '2', '2', '2', '2', '2', '2', '2', '2', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,518 | ```python
state * (feats[6] + 2*feats[7]) * (feats[36] + 2*feats[37])
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state * (feats[7] + 2*feats[36])
``` | ```python
state * (feats[7] or (2 * feats[36]))
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.56%
Enrollment gestational age (11-20): 17.28%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.50%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.84%
Enrollment gestational age (11-20): 17.25%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.55%
Enrollment delivery status (1-1): ... | 0.9 | ['1', '1', '1', '1', '1', '1', '1', '2', '1', '1'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,519 | ```python
state + 5 * state * (feats[7] and 3 * (feats[36] + feats[37]))
``` |
llm-Focus on the young mothers by age and also focus on those with low income | ```python
state + state * (feats[7] and feats[36]) + 0.5 * state * (feats[8] and feats[36])
``` | ```python
state * (feats[7] and feats[36]) + state * 0.5 * (feats[8] and feats[36]) + state * 0.25
``` |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.43%
Enrollment gestational age (11-20): 17.62%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.13%
Enrollment delivery status (1-1): ... |
Category: Enrollment gestational age
Enrollment gestational age (0-10): 28.53%
Enrollment gestational age (11-20): 17.42%
Enrollment gestational age (21-30): 0.00%
Enrollment gestational age (31-40): 0.00%
Category: Enrollment delivery status
Enrollment delivery status (0-0): 85.37%
Enrollment delivery status (1-1): ... | 0.5 | ['2', '2', '1', '2', '2', '1', '1', '1', '1', '2'] | llm-Focus on the young mothers by age and also focus on those with low income | 0 | [INST] Create a Python reward function for Reinforcement Learning, with the objective of prioritizing higher states and: Focus on the young mothers by age and also focus on those with low income. The function should use 'state' (value is either 0,1) and features 'feats' (length 43 array) to direct the Reinforcement Lea... | 7,520 | ```python
(state + 5) * (feats[7] + 3 * feats[36]) + 2 * (state + 1) * (1 - feats[36]) * (feats[8] + feats[9])
``` |
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