Based on prior knowledge, we can analyze the relationship between the feature "installment_commitment" and the target variable "Does this person receive a credit?".

Generally, a higher installment commitment, which represents a higher percentage of disposable income, may indicate a higher risk for lenders. Therefore, it is possible that individuals with high installment_commitment may be less likely to receive a credit ("no" class), while those with low installment_commitment may have a higher chance ("yes" class).

To create the dictionary with the required format, we need to determine the ranges of values for each target class. Let's assume the following ranges:

- "yes" class (receive credit): installment_commitment <= 30%
- "no" class (do not receive credit): installment_commitment >= 40%

Based on these ranges, we can provide typical installment_commitment values for each target class.

```json
{
	"yes": [10.5, 15.2, 20.0, 25.8, 28.7],
	"no": [41.9, 45.6, 50.3, 55.0, 58.2]
}
```

Please note that these ranges and values are hypothetical and may vary depending on the specific context and dataset. The provided values are only examples and should not be considered as definitive or accurate for any specific case.