Based on prior knowledge, we can analyze the relationship between the "lug_boot" feature and the task of rating the decision to buy a car. 

Intuitively, the size of the luggage boot might affect the perceived quality and usefulness of the car, and hence, the rating of the decision to buy it. Let's analyze the possible relationship between the "lug_boot" feature and the target rating classes.

We will analyze the relationship by examining the distribution of lug_boot values for each rating class. Here is the analysis:

- For the "unacceptable" rating class:
  - Possible values of "lug_boot": ['small', 'med', 'big']

- For the "acceptable" rating class:
  - Possible values of "lug_boot": ['small', 'med', 'big']

- For the "good" rating class:
  - Possible values of "lug_boot": ['small', 'med', 'big']

- For the "very good" rating class:
  - Possible values of "lug_boot": ['small', 'med', 'big']

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

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

This dictionary indicates that all possible values of the "lug_boot" feature are present for each rating class.