Based on prior knowledge, the installment_commitment feature represents the percentage of disposable income that is allocated towards installment payments. To analyze the relationship between this feature and the task of whether a person receives credit or not, we can examine the distribution of installment_commitment values for each target class.

Let's assume that the target class 'yes' represents individuals who receive a credit, and the target class 'no' represents individuals who do not receive a credit. We can create a dictionary that includes the typical installment_commitment values for each target class. Here's the formatted dictionary:

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

In this example, the 'yes' class typical installment_commitment values are [15.0, 20.0, 25.0, 30.0, 35.0], while the 'no' class typical installment_commitment values are [5.0, 10.0, 35.0, 40.0, 45.0]. These values can provide an initial understanding of how installment_commitment relates to the likelihood of receiving a credit.