Based on the information provided, we have a categorical feature "buying price" with four categories: 'very high', 'high', 'medium', and 'low'. The task is to rate the decision to buy the car as unacceptable, acceptable, good, or very good.

To analyze the relationship between the feature and the target, we can consider the possible associations between buying price and the decision rating. Here is a potential analysis:

1. Unacceptable: It's possible that cars with a "very high" buying price are more likely to be rated as unacceptable, as they might be considered overpriced or not worth the investment. It is also possible that cars with a "high" buying price could be rated as unacceptable if they do not meet the buyer's expectations. 

2. Acceptable: Cars with a "medium" or "low" buying price might be more likely to be rated as acceptable, as they are considered affordable or reasonably priced. 

3. Good: Cars with a "medium" buying price could be rated as good, as they strike a balance between affordability and good value for money. 

4. Very good: It is possible that cars with a "low" buying price may be rated as very good, as they are considered a great deal or offer exceptional value.

Based on this analysis, we can create a dictionary with the possible values of the "buying price" feature for each target class:

```json
{
	"unacceptable": ["very high", "high"],
	"acceptable": ["medium", "low"],
	"very good": ["low"],
	"good": ["medium"]
}
```

Note that for the "unacceptable" class, we included the values "very high" and "high". For the "acceptable" class, we included the values "medium" and "low". For the "very good" class, we included only the value "low". And for the "good" class, we included only the value "medium".

It's important to mention that this analysis is based on general assumptions and should be validated with actual data before making any conclusive statements.