Based on prior knowledge, it is known that skin thickness can be an important factor in assessing the risk of diabetes. Higher skin thickness values can indicate a higher likelihood of diabetes.

Based on this understanding, an analysis of the relationship between the feature (SkinThickness) and the task (diabetes prediction) can be performed. The analysis would involve comparing the typical ranges of SkinThickness values for individuals with and without diabetes.

Here is a dictionary with 5 typical SkinThickness values for each target class ('yes' and 'no'):

```json
{
	"yes": [32.0, 35.0, 38.0, 40.0, 42.0],
	"no": [20.0, 22.0, 25.0, 28.0, 30.0]
}
```

Please note that the values provided above are just examples and may need to be adjusted based on the specific dataset and the target variable's distribution.