Based on prior knowledge, we can analyze the relationship between the age feature and the client's subscription to a term deposit. It is reasonable to assume that there might be certain age ranges that are more likely to result in a subscription.

Here is a dictionary that represents the relationship between the feature "age" and the target variable:

```json
{
	"no": [25.0, 30.0, 35.0, 40.0, 45.0],
	"yes": [35.0, 40.0, 45.0, 50.0, 55.0]
}
```

In the "no" category, we have typical age values of 25.0, 30.0, 35.0, 40.0, and 45.0. These represent ages that are less likely to result in a term deposit subscription. 

In the "yes" category, we have typical age values of 35.0, 40.0, 45.0, 50.0, and 55.0. These represent ages that are more likely to result in a term deposit subscription.

Please note that these age values are just hypothetical examples based on common trends and are not specific to any dataset. The actual values may vary depending on the dataset and the target population.