Based on my prior knowledge, I can analyze the relationship between the feature 'RIAGENDR' and the task of predicting a person's age group.

In this case, the feature 'RIAGENDR' represents the respondent's gender, with 1 representing male and 2 representing female. To determine the appropriate ranges of values for each age group, we can consider common age categories such as 'Adult' and 'Senior'.

After analyzing the relationship, I can create a dictionary with the following format:

```json
{
    "Adult": [1.0, 1.0, 2.0, 2.0, 2.0],  
    "Senior": [1.0, 1.0, 1.0, 2.0, 2.0]  
}
```

In this dictionary, the key 'Adult' represents the target class for adults, and the key 'Senior' represents the target class for seniors. The values in the lists are floats representing the possible values of the 'RIAGENDR' feature for each target class.

Please note that the values in the lists are just examples. To provide a more accurate and representative analysis, it would be necessary to analyze the actual data and calculate the appropriate values based on the distribution of the 'RIAGENDR' feature for each target class.