Based on prior knowledge, we can analyze the relationship between the "campaign" feature and the task of whether the client subscribes to a term deposit or not.

Typically, the number of contacts performed during a campaign can vary based on client behavior and preferences. Generally, clients who are more engaged and interested in the offer tend to require fewer contacts to subscribe, while those who are less interested may require more contacts.

Here is an analysis of the relationship between the "campaign" feature and the target variable:

- For clients who did not subscribe to a term deposit ("no" class), typical campaign values could be: 1, 2, 3, 4, 5.
- For clients who subscribed to a term deposit ("yes" class), typical campaign values could be: 1, 2, 3, 4, 5.

Based on this analysis, let's create a dictionary with the required format:

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

Note: The actual values for the "campaign" feature may vary depending on the dataset and specific analysis, but this example provides a general understanding of the relationship between the feature and the target variable.