Based on prior knowledge, the feature "Frequency" represents the total number of blood donations made by a person. To analyze the relationship between this feature and the task of donating blood, we can consider the following:

1. Donors who donate blood regularly may have a higher frequency of donations compared to those who do not.
2. Donors who donate blood for specific reasons (e.g., medical emergencies) may have higher frequencies.
3. Non-donors may have a lower frequency of donations, possibly zero.

Based on this analysis, we can create a dictionary with the specified format:

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

In the dictionary, the "yes" key represents the target class 'yes' (donated blood), and the "no" key represents the target class 'no' (did not donate blood). The values associated with each key are lists of float values representing possible values of the feature "Frequency" for each target class.

For the "yes" target class, a person who donated blood may have various frequency values like 1.0, 2.0, 3.0, 4.0, and 5.0 representing different frequencies of donations.

For the "no" target class, a person who did not donate blood may have a frequency value of 0.0 as they have not made any donations.

Note: The specific values provided in the dictionary are examples and may not accurately represent the true relationship between the feature and the task. Further analysis and data exploration would be required to generate more accurate values.