Based on prior knowledge, we can assume that the Recency feature represents the number of months since the person's last blood donation. To analyze the relationship between the Recency feature and the target variable (Did the person donate blood?), we can consider the following insights:

1. People who recently donated blood are more likely to donate again in the future, as they are actively engaged in the donation process. Therefore, we would expect a higher proportion of "yes" values for smaller values of Recency.

2. On the other hand, people who donated blood a long time ago might have various reasons for not donating again, such as medical conditions, age, or personal preferences. Consequently, we would expect a higher proportion of "no" values for larger values of Recency.

Based on these insights, we can create a dictionary that includes the possible values of the Recency feature for both "yes" and "no" target classes:

```json
{
	"yes": [0.5, 1.5, 2.5, 3.5, 4.5],
	"no": [20.5, 21.5, 22.5, 23.5, 24.5]
}
```

Please note that the selected values for each class are just illustrative examples, and the actual values may vary depending on the dataset.