Based on prior knowledge, the feature "Pregnancies" is related to the target variable "Does this person have diabetes?" because multiple pregnancies can potentially increase the risk of developing diabetes.

Now let's analyze the relationship between the feature "Pregnancies" and the target variable "Does this person have diabetes?" to create the dictionary.

Considering that the target variable has two possible classes, 'yes' and 'no', we will determine the range of values for each class based on general knowledge about pregnancies and diabetes.

The values for 'yes' class (indicating that the person has diabetes) will include higher values of pregnancies, as multiple pregnancies can be a risk factor for diabetes. The values for 'no' class (indicating that the person does not have diabetes) will include lower values of pregnancies, as lower values suggest a lower risk of diabetes.

Let's generate the dictionary:

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

In the dictionary, the "yes" class includes typical pregnancies values ranging from 0 to 4, reflecting a potential risk of diabetes. The "no" class also includes typical pregnancies values ranging from 0 to 4, reflecting a lower risk of diabetes.

Please note that the actual typical range of values may differ depending on the specific dataset and its characteristics.