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

It is reasonable to assume that individuals who donate blood more frequently are more likely to donate blood again in the future. Therefore, we can expect a positive relationship between the frequency of blood donations and the likelihood of a person donating blood again.

To generate the dictionary, we will consider typical ranges and values for the feature "Frequency" and determine the corresponding target classes.

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

For the "yes" class, we can assume that individuals who have donated blood will have a range of frequencies such as 1.0, 2.0, 3.0, 4.0, and 5.0. These values represent typical frequencies for individuals who have already donated blood.

For the "no" class, we can assume that individuals who have not donated blood will have a range of frequencies such as 0.0, 0.5, 1.5, 2.5, and 3.5. These values represent typical frequencies for individuals who have not donated blood.

Please note that these values are just examples and may not accurately represent the actual frequency values in the dataset you are working with. It is always recommended to perform thorough analysis and data exploration before making any assumptions about the relationship between features and target variables.