Based on prior knowledge, we can analyze the relationship between the feature "installment_commitment" and the task of determining whether a person receives credit or not.

Considering the feature "installment_commitment" as the installment rate in percentage of disposable income, we can assume that the higher the installment rate, the less likely a person would receive credit. On the other hand, a lower installment rate indicates a higher chance of receiving credit.

Based on this analysis, we can generate a dictionary with five typical installment_commitment values for each target class:

```json
{
	"yes": [5.0, 10.0, 15.0, 20.0, 25.0],
	"no": [40.0, 45.0, 50.0, 55.0, 60.0]
}
```

This dictionary represents possible values of the feature "installment_commitment" for the target classes "yes" and "no". It includes typical installment_commitment values that are higher for the "no" class, and lower for the "yes" class.