Based on prior knowledge, the feature "pdays" represents the number of days that have passed after the client was last contacted from a previous campaign. We can analyze the relationship between the feature and the task by examining typical values of "pdays" for each target class.

In order to determine the appropriate ranges of values, we can assume that a lower "pdays" value indicates a more recent contact with the client, while a higher value indicates a longer time since the last contact.

Here is an analysis of the relationship between the "pdays" feature and the task:

For the "no" class (clients who did not subscribe to a term deposit):
Typical values of "pdays" can range from 0 to a high value (e.g., 999).
Example values for the "no" class could be [0.0, 10.0, 20.0, 50.0, 100.0], where 0.0 represents a recent contact and 100.0 represents a contact that happened quite a while ago.

For the "yes" class (clients who subscribed to a term deposit):
Typical values of "pdays" can range from 0 to a high value (e.g., 999).
Example values for the "yes" class could be [0.0, 5.0, 15.0, 30.0, 60.0], where 0.0 represents a recent contact and 60.0 represents a contact that happened quite a while ago.

Based on this analysis, here is the generated dictionary:

```json
{
	"no": [0.0, 10.0, 20.0, 50.0, 100.0],
	"yes": [0.0, 5.0, 15.0, 30.0, 60.0]
}
```

Please note that the specific values provided in the dictionary are just examples and can vary based on the dataset and the distribution of the feature values.