Based on prior knowledge, it is expected that higher safety ratings would generally lead to a more positive rating for the decision to buy the car. Therefore, we can assume that cars with higher safety ratings are more likely to be rated as "good" or "very good" while cars with lower safety ratings are more likely to be rated as "unacceptable" or "acceptable". 

To create the dictionary, we need to identify the possible values of the feature "safety" for each target class. Here's how the dictionary would look:


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

Explanation:
- Cars with "low" safety ratings are more likely to be rated as "unacceptable". 
- Cars with "low" or "med" safety ratings are more likely to be rated as "acceptable". 
- Cars with "high" safety ratings are more likely to be rated as "very good". 
- Cars with "med" or "high" safety ratings are more likely to be rated as "good".

It's worth noting that this analysis and the resulting dictionary are based on assumptions and prior knowledge. Further analysis and modeling would be necessary to confirm the relationship between the feature and target variables.