Based on prior knowledge, we can analyze the relationship between the feature "safety" and the target variable "decision to buy this car". Here's a possible analysis:

- Unacceptable: This target class represents a negative or highly dissatisfied decision to buy the car. It is likely that cars with low safety ratings would fall into this category. Therefore, the "unacceptable" target class would have the safety values "low" as a possible value.

- Acceptable: This target class represents a somewhat satisfied decision to buy the car. Cars with moderate safety ratings might be considered "acceptable". Therefore, the "acceptable" target class would have the safety values "med" as a possible value.

- Good: This target class represents a positive and satisfied decision to buy the car. Cars with high safety ratings would most likely fall into this category. Therefore, the "good" target class would have the safety value "high" as a possible value.

- Very Good: This target class represents an extremely positive and highly satisfied decision to buy the car. It is possible that cars with superior safety ratings would be classified as "very good". Therefore, the "very good" target class would have the safety value "high" as a possible value.

Based on this analysis, we can create the following dictionary:

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