Based on prior knowledge, we can analyze the relationship between the "Pregnancies" feature and the task of determining whether a person has diabetes.

Analysis:
- For the target class "yes", we can expect that individuals with a higher number of pregnancies may have a higher likelihood of having diabetes. This is because pregnancy can contribute to the development of gestational diabetes in women, which can later progress to type 2 diabetes.
- For the target class "no", we can expect that individuals with a lower or zero number of pregnancies may have a lower likelihood of having diabetes. This is because pregnancy-related factors would not contribute to the development of diabetes in these individuals.

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

```json
{
  "yes": [2.0, 6.0, 3.0, 4.0, 5.0],
  "no": [0.0, 1.0, 3.0, 0.0, 2.0]
}
```

In the dictionary above, we have included 5 typical values of the "Pregnancies" feature for each target class. The values for the "yes" class are [2.0, 6.0, 3.0, 4.0, 5.0], and the values for the "no" class are [0.0, 1.0, 3.0, 0.0, 2.0]. These values represent different ranges of pregnancies for individuals in each class.