Based on prior knowledge, we can analyze the relationship between the "age" feature and the task of whether the client subscribes to a term deposit. Let's assume that younger individuals are less likely to subscribe to a term deposit, while older individuals are more likely to subscribe.

Based on this assumption, we can create a dictionary with typical age values for each target class ('no' and 'yes') as follows:

```json
{
    "no": [20.0, 25.0, 30.0, 35.0, 40.0],
    "yes": [55.0, 60.0, 65.0, 70.0, 75.0]
}
```

In this example, we selected five age values for each target class, representing typical ages of clients who did not subscribe ('no') and those who did subscribe ('yes') to a term deposit. These values are for illustrative purposes only and may vary in a real-world scenario.