Based on the description provided, we want to analyze how the feature "purpose" relates to the task of whether a person receives credit.

To conduct the analysis, we can examine the distribution of different purposes for both the "yes" and "no" target classes.

Here is the dictionary that represents the relationship between the "purpose" feature and the target variable:

```json
{
	"yes": ["radio/tv", "education", "furniture/equipment", "new car", "used car", "business", "domestic appliance", "repairs", "retraining"],
	"no": ["other"]
}
```

Explanation:
- The "yes" class includes individuals who receive credit.
- The "no" class includes individuals who do not receive credit.

Based on existing knowledge, we can allocate the following purposes for each target class:
- The "yes" class can include all the categories present in the "purpose" feature.
- The "no" class has a value of "other" as an additional category that is not present in the "yes" class.

Please note that this analysis assumes no specific information on the relationship between purpose and credit approval and is based solely on the available information.