Based on the given feature "previous" and the task of determining if a client subscribes to a term deposit, we can analyze the relationship between the feature and the target using our prior knowledge.

Generally, the number of contacts performed before the current campaign can provide insight into a client's likelihood to subscribe to a term deposit. By examining the values of the "previous" feature for each target class ('no' and 'yes'), we can identify any patterns or trends.

Here is a dictionary containing the possible values of the "previous" feature for each target class:

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

Typically, a few values can be observed as representative of each target class. By including these typical values in the dictionary, we can provide a better understanding of the relationship between the "previous" feature and the target.

For the target class 'no', some typical previous values could be [0.0, 1.0, 2.0, 3.0, 4.0]. These values indicate that the respective clients have not been contacted or have only had limited contact before the current campaign. 

Similarly, for the target class 'yes', the typical previous values could also be [0.0, 1.0, 2.0, 3.0, 4.0]. These values suggest that clients who subscribe to a term deposit have varied but generally low previous contact numbers.

Please note that the provided values are just examples and may not necessarily represent the actual distribution of the feature across target classes. The actual values may depend on the specific dataset used for analysis.