Based on prior knowledge, we can assume that age may be a relevant feature in determining whether a person has diabetes or not. Younger individuals may have a lower likelihood of having diabetes while older individuals may have a higher likelihood.

To analyze the relationship between the Age feature and the task of determining if a person has diabetes, we can start by examining the typical age ranges for individuals with and without diabetes.

Here is an analysis of the relationship between the Age feature and the task:

- For individuals with diabetes ('yes'):
  - Typical Age values could range from around 20 to 70 years old, with higher values centered around 50 to 60 years old.
  - Possible Age values for this target class may include [38.5, 45.2, 51.7, 59.3, 62.9] as examples.
  
- For individuals without diabetes ('no'):
  - Typical Age values could range from around 20 to 90 years old, with higher values centered around 30 to 70 years old.
  - Possible Age values for this target class may include [22.1, 36.7, 46.8, 53.9, 67.2] as examples.

Based on this analysis, we can create the following dictionary:

```json
{
    "yes": [38.5, 45.2, 51.7, 59.3, 62.9],
    "no": [22.1, 36.7, 46.8, 53.9, 67.2]
}
```