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

Typically, higher installment commitment rates could indicate that the person has a higher financial burden and may struggle to make payments. As a result, it is possible that individuals with higher installment commitment rates are less likely to receive credit.

Additionally, lower installment commitment rates could suggest that the person has a lower financial burden and may have a higher capacity to make payments. Therefore, individuals with lower installment commitment rates are more likely to receive credit.

Based on this analysis, we can create a dictionary that includes 5 typical installment_commitment values for each target class:

```json
{
	"yes": [5.0, 10.0, 15.0, 20.0, 25.0],
	"no": [30.0, 35.0, 40.0, 45.0, 50.0]
}
```

Please note that the values provided in the dictionary are just examples and may not reflect the actual relationship between the feature and target variable. It is necessary to perform further analysis and exploratory data analysis to validate the relationship.