Based on prior knowledge and intuition, the feature "num_dependents" may have a relationship with the task of whether a person receives credit or not. Generally, individuals with more dependents may have higher expenses and financial obligations, which could affect their creditworthiness. 

After analyzing the relationship between the feature "num_dependents" and the target variable, we can create a dictionary with typical values for each target class.

```json
{
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0],
	"no": [0.0, 1.0, 2.0, 3.0, 5.0]
}
```

In this dictionary, we assume that "yes" corresponds to individuals who do receive credit, while "no" corresponds to individuals who do not receive credit. The values in the lists represent typical values for the "num_dependents" feature for each target class.