Based on my prior knowledge, I can analyze the relationship between the feature "previous" (number of contacts performed before this campaign and for this client) and the task of whether the client subscribes to a term deposit.

Analysis:
The feature "previous" represents the number of contacts performed on the client before this campaign. In general, it is possible to assume that clients who have been contacted more often in the past may be more likely to subscribe to a term deposit. However, there may be a point at which too many contacts have been made and the client becomes less likely to subscribe.

To generate the dictionary with specific details, I will analyze the values of the "previous" feature for each target class: 'no' and 'yes'.

- For target class 'no':
    - Possible values of feature previous for target class 'no': [0, 1, 2, 3, 4]
    - These values represent the number of previous contacts for clients who did not subscribe to a term deposit. Since these clients did not subscribe, it is likely that they were not interested in a term deposit even after being contacted a few times.

- For target class 'yes':
    - Possible values of feature previous for target class 'yes': [0, 1, 2, 3, 4]
    - These values represent the number of previous contacts for clients who subscribed to a term deposit. It is possible that clients who subscribed were convinced to do so after being contacted multiple times.

Based on this analysis, the dictionary with specific details will be:

```json
{
	"no": [0.0, 1.0, 2.0, 3.0, 4.0],
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0]
}
```