Upon analyzing the relationship between the feature "Frequency" (total number of donations) and the task of whether the person donated blood or not, we can make an assumption based on common knowledge. It is generally expected that individuals who donated blood multiple times are more likely to donate blood again in the future compared to those who donated only a few times or never donated before.

Based on this assumption, we can create a dictionary that presents typical Frequency values for each target class ('yes' and 'no') as follows:

```json
{
    "yes": [1.0, 2.0, 3.0, 4.0, 5.0],
    "no": [0.0, 0.5, 1.5, 2.5, 3.5]
}
```

In this dictionary, we assume that individuals who have donated blood at least once (Frequency = 1.0) are more likely to donate blood again in the future than individuals who never donated before (Frequency = 0.0). For the 'yes' class, we include typical Frequency values such as 2.0, 3.0, 4.0, and 5.0 to represent individuals who have donated multiple times. The 'no' class includes Frequency values such as 0.5, 1.5, 2.5, and 3.5 to represent individuals who have donated blood fewer times.

Please note that these values are just examples based on the assumption mentioned earlier, and the actual relationship between Frequency and the task may vary depending on the dataset.