Based on prior knowledge, we can analyze the relationship between the "campaign" feature and the target variable of whether the client subscribes to a term deposit.

The "campaign" feature represents the number of contacts performed during the campaign for a particular client. To determine the appropriate ranges of values, we can assume that a higher number of contacts might indicate persistence or urgency in convincing the client to subscribe. 

Let's analyze the relationship between the "campaign" feature and the target variable:

- Clients with a low number of contacts in the campaign may be less likely to subscribe.
- Clients with a moderate number of contacts in the campaign may have a higher likelihood of subscribing, as the repeated contacts could indicate interest but not urgency.
- Clients with a high number of contacts in the campaign may have a lower likelihood of subscribing, as it might be indicative of annoyance or disinterest.

Based on this analysis, we can create a dictionary with the specific details:

```json
{
	"no": [1.0, 2.0, 3.0, 4.0, 5.0],
	"yes": [1.0, 2.0, 3.0, 4.0, 5.0]
}
```

For the target class "no" (clients who did not subscribe), typical values for the "campaign" feature could be 1.0, 2.0, 3.0, 4.0, and 5.0. Similarly, for the target class "yes" (clients who subscribed), typical values for the "campaign" feature could also be 1.0, 2.0, 3.0, 4.0, and 5.0.