Based on prior knowledge, we can assume that the feature "existing_credits" refers to the number of existing credits that a person has at the bank. To analyze the relationship between this feature and the task of whether the person receives a credit or not, we can look for patterns in the data.

It is reasonable to assume that individuals with a higher number of existing credits at the bank might find it more challenging to receive additional credit. On the other hand, individuals with a lower number of existing credits may have a higher chance of receiving credit.

Here is the dictionary with the possible values of the feature "existing_credits" for each target class:

```json
{
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0], 
	"no": [2.0, 3.0, 4.0, 5.0, 6.0]
}
```

In this case, for "yes" (individuals who receive a credit), some possible values for the "existing_credits" feature could be 0, 1, 2, 3, and 4. For "no" (individuals who do not receive a credit), some possible values for the "existing_credits" feature could be 2, 3, 4, 5, and 6.

Please note that these values are assumed based on general expectations and prior knowledge. The actual relationship between the feature and the target variable should be confirmed by analyzing the dataset or considering domain-specific knowledge.