Based on prior knowledge and experience with diabetes data, I can analyze the relationship between the "SkinThickness" feature and whether a person has diabetes or not.

Typically, skinfold thickness is measured in several areas of the body including the triceps, biceps, subscapular, and iliac crest. These measurements can provide information about body fat distribution and can be indicative of health conditions such as diabetes.

In the case of diabetes, it is commonly observed that individuals with higher levels of body fat tend to have a higher risk of developing diabetes. Therefore, it can be hypothesized that individuals with higher skinfold thickness measurements may be more likely to have diabetes.

To create the dictionary with specific details, let's assume that we have access to a dataset that contains information about skinfold thickness and whether each individual has diabetes or not.

Here is a markdown code snippet showing an example of the dictionary format:

```json
{
	"yes": [28.0, 33.0, 40.0, 35.0, 42.0],
	"no": [15.0, 18.0, 20.0, 22.0, 25.0]
}
```

In this example, I have included 5 typical values for each target class, "yes" and "no". These values represent the skinfold thickness measurements (in mm) for individuals with and without diabetes, respectively.

Please note that the specific values provided in the dictionary are for illustrative purposes only and may not represent actual data. The actual values would need to be derived from the dataset being analyzed.