Based on the feature description and the task, we need to analyze the relationship between the credit history and whether the person receives a credit.

To do this, we can examine the distribution of different credit history categories for both the "yes" and "no" classes. Let's assume the dataset contains information on whether a person receives a credit ("yes" or "no") and their credit history (categorical variable with categories: ['critical/other existing credit', 'existing paid', 'delayed previously', 'no credits/all paid', 'all paid']).

After analyzing the relationship, we can create a dictionary with the possible credit history values for each target class.

Here is an example of the dictionary:

```json
{
	"yes": ["existing paid", "delayed previously", "no credits/all paid", "all paid"],
	"no": ["critical/other existing credit"]
}
```

This dictionary assumes that the "credit_history" values of "existing paid", "delayed previously", "no credits/all paid", and "all paid" are indicative of a person receiving a credit ("yes" class), while the value "critical/other existing credit" indicates a person not receiving a credit ("no" class).

Please note that this analysis is based on prior knowledge and assumptions, and the actual relationship between the feature and the target variable may vary depending on the dataset and context.