Based on prior knowledge, I can analyze the relationship between the feature "age" and the task of whether the client subscribes to a term deposit.

I would expect that age could be a significant factor in determining whether a client subscribes to a term deposit. Generally, younger individuals might be less inclined to invest in long-term commitments, whereas older individuals might have a greater interest in securing their financial future.

To analyze the relationship, I would consider the age range and observe how the distribution of ages varies between the two target classes: "no" (client does not subscribe to a term deposit) and "yes" (client subscribes to a term deposit).

Based on this analysis, here is the output dictionary:

```json
{
	"no": [22.0, 30.0, 35.0, 40.0, 50.0],
	"yes": [45.0, 47.0, 55.0, 60.0, 65.0]
}
```

In the "no" class, I have included ages 22.0, 30.0, 35.0, 40.0, and 50.0 as typical age values. These values represent a range of younger to middle-aged individuals who are less likely to subscribe to a term deposit.

In the "yes" class, I have included ages 45.0, 47.0, 55.0, 60.0, and 65.0 as typical age values. These values represent a range of middle-aged to older individuals who are more likely to subscribe to a term deposit.

Please note that the specific values provided here are examples and should be adjusted based on the actual distribution of age in the dataset.