Based on my prior knowledge and understanding, the feature "previous" represents the number of contacts performed before the current campaign and for a particular client. It is a numeric variable that indicates the previous engagement of the client with previous marketing campaigns.

To analyze the relationship between the feature "previous" and the target variable "Does this client subscribe to a term deposit?," we need to examine how the values of "previous" differ for the two target classes 'no' and 'yes'.

Here is an example dictionary that represents the relationship between the feature and target:

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

For the target class 'no', possible values of the feature "previous" could be 0.0, 1.0, 2.0, 3.0, and 4.0. These values suggest that clients who did not subscribe to a term deposit generally had a range of previous contacts, with the most common values being 0.0, 1.0, and 2.0.

Similarly, for the target class 'yes', possible values of the feature "previous" could also be 0.0, 1.0, 2.0, 3.0, and 4.0. This indicates that clients who did subscribe to a term deposit also had a range of previous contacts, with similar common values as the 'no' class.

Please note that these values are just examples to demonstrate the schema of the desired output dictionary. In a real analysis, it would be important to explore the actual data and calculate the statistics of "previous" for both target classes to determine more accurate typical values.