Based on prior knowledge, we can analyze the relationship between the feature "Frequency" and the task "Did the person donate blood?". 

It is reasonable to assume that the "Frequency" feature represents the total number of times a person has donated blood. We can use this feature to understand the donation behavior of individuals and how it relates to their likelihood of donating blood again.

A higher value of "Frequency" may indicate a more frequent donor, and hence, a higher probability of donating blood again. On the other hand, a lower value of "Frequency" may indicate individuals who have not donated frequently, and thus, may be less likely to donate blood.

Based on this analysis, let's create a dictionary with the typical values of "Frequency" for each target class:

```json
{
	"yes": [5.0, 3.0, 4.0, 7.0, 6.0],
	"no": [1.0, 2.0, 0.0, 8.0, 9.0]
}
```

These typical values are just examples and can be adjusted based on the specific dataset and understanding of blood donation patterns.