Based on my prior knowledge, the credit_history feature is likely to have a relationship with the task of determining whether a person receives a credit or not. The credit history of an individual is typically a factor that lenders consider when making a decision.

To analyze the relationship in more detail, I would examine the distribution of credit history categories among the two target classes (yes and no). By doing so, I can identify which credit history categories are more prevalent among individuals who receive credit and those who do not.

Here is a possible dictionary based on the analysis:

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

In this dictionary, the "yes" class includes the credit history categories "existing paid", "no credits/all paid", and "all paid", which are more likely to be associated with individuals who receive credit. Conversely, the "no" class includes the categories "critical/other existing credit" and "delayed previously", which are more likely to be associated with individuals who do not receive credit.

Please note that this analysis is based on general knowledge and assumptions. It is important to conduct a more comprehensive analysis using additional data and context specific to the credit-related task at hand to provide a more accurate and robust relationship analysis.