Based on my prior knowledge and understanding of credit applications, the relationship between the credit_amount feature and the task of determining whether a person receives a credit can be analyzed as follows:

1. Generally, individuals with higher credit amounts are more likely to receive a credit. This is because higher credit amounts often signify a greater ability to repay the loan.

2. However, there might be a threshold beyond which the credit amount becomes too high and may result in the loan application being rejected. This can happen if the credit amount exceeds the maximum limit set by the lender based on their risk assessment.

3. On the other hand, individuals with very low credit amounts may also face a higher chance of loan rejection. This can happen if the credit amount is considered too small by the lender, resulting in the loan application being deemed not worth the administrative costs or risks involved.

Based on this analysis, the dictionary containing the specific details for the credit_amount feature can be generated:

```json
{
	"yes": [1000.0, 2000.0, 3000.0, 4000.0, 5000.0],
	"no": [100.0, 500.0, 10000.0, 15000.0, 20000.0]
}
```

The list of float values provided in the dictionary represents typical credit_amount values for each target class. The "yes" class includes credit amounts such as 1000.0, 2000.0, 3000.0, 4000.0, and 5000.0, whereas the "no" class includes credit amounts such as 100.0, 500.0, 10000.0, 15000.0, and 20000.0. Please note that the values provided in the dictionary are hypothetical examples and may vary depending on the specific dataset being analyzed.