Based on prior knowledge, we can analyze the relationship between age and whether a person receives credit or not. Typically, there is no universal range of values for determining credit approval based on age, as it can vary depending on the specific context, policies, and regulations.

Nonetheless, let's assume that individuals below 18 years of age are generally not eligible for credit. Using this information, we can identify age ranges that may correspond to the classification task:

- Age range (0-17): Assuming individuals below 18 are not approved for credit, we can assign them to the "no" class.
- Age range (18 and above): Individuals within this range may be eligible for credit, so we can assign them to the "yes" class.

Based on this analysis, we can create a dictionary with the following format:

```json
{
	"yes": [18.0, 19.0, 20.0, 21.0, 22.0],
	"no": [0.0, 1.0, 2.0, 3.0, 4.0]
}
```

Please note that the age values presented here are just examples and may not reflect the actual distribution of ages in the dataset.