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

Typically, the "age" feature may have different ranges of values that can affect the likelihood of a client subscribing to a term deposit. Let's assume that young clients are less likely to subscribe, while middle-aged and older clients are more likely to subscribe.

Based on this analysis, we can create a dictionary as follows:

```json
{
	"no": [25.0, 26.0, 27.0, 28.0, 29.0],
	"yes": [50.0, 55.0, 60.0, 65.0, 70.0]
}
```

This dictionary suggests that for the 'no' class, the possible values of the feature "age" can range from 25.0 to 29.0, while for the 'yes' class, the possible values can range from 50.0 to 70.0. These values are just examples and could vary depending on the data and analysis.