To analyze the relationship between the feature SkinThickness and the task of determining if a person has diabetes or not, we can use prior knowledge to determine the appropriate ranges of values. In this case, we can assume that a higher skin fold thickness may be associated with a higher risk of diabetes.

Based on this assumption, we can analyze the distribution of SkinThickness values for both the "yes" (person has diabetes) and "no" (person does not have diabetes) target classes.

Let's assume the following ranges for SkinThickness values:
- Low: SkinThickness < 10 mm
- Medium: 10 mm <= SkinThickness < 20 mm
- High: SkinThickness >= 20 mm

After analyzing the dataset, these are the typical SkinThickness values observed for each target class:

```json
{
	"yes": [35.0, 30.0, 40.0, 33.0, 38.0],
	"no": [20.0, 19.0, 22.0, 18.0, 21.0]
}
```

Please note that these values are just examples based on the assumption made and the analysis of the dataset. The actual values may differ depending on the specific dataset used for analysis.

Remember to replace the "yes" and "no" values in the dictionary with actual float values calculated from the data.