Based on prior knowledge, it is difficult to determine how the size of the luggage boot (lug_boot) would directly impact the rating of the decision to buy a car. However, we can analyze the relationship between the feature and target variable by examining the distribution of lug_boot values for each target class.

To generate the dictionary, we need to determine the possible values of lug_boot for each target class. Here's an example of how we can analyze this relationship:

- Unacceptable: It is hard to predict any specific lug_boot values that would generally categorize a decision as "unacceptable". However, we can include all possible values of lug_boot for this target class.

- Acceptable: Similar to the previous case, it is hard to predict any specific lug_boot values that would generally categorize a decision as "acceptable". We can include all possible values of lug_boot for this target class.

- Good: Again, the size of the luggage boot may not have a clear specific impact on categorizing a decision as "good". However, we can include all possible values of lug_boot for this target class.

- Very Good: As before, it is difficult to predict specific lug_boot values for this target class. We can include all possible values of lug_boot for this category.

Based on this analysis, the generated dictionary in markdown format would be:

```json
{
	"unacceptable": ["small", "med", "big"],
	"acceptable": ["small", "med", "big"],
	"very good": ["small", "med", "big"],
	"good": ["small", "med", "big"]
}
```

Please note that in this analysis, we assumed that all possible values of lug_boot ("small", "med", "big") can be included in each target class category, as it is difficult to predict any specific values for a given target class.