Based on the description, the feature "SEQN" is a respondent sequence number, which is a numeric variable. To determine the appropriate ranges of values for each age group, we need to analyze the relationship between the feature and the target variable.

To do this, we will need a data set with records that include the SEQN feature and the corresponding age group (senior or non-senior). Without the actual data set, it's difficult to provide an accurate analysis of the relationship between the feature and the target.

However, assuming that there is a relationship between the SEQN feature and age group, we can still generate a dictionary with some example values for each target class:

```json
{
  "Adult": [1.0, 2.0, 3.0, 4.0, 5.0],
  "Senior": [100.0, 101.0, 102.0, 103.0, 104.0]
}
```

Please note that these example values are arbitrary and may not reflect the actual relationship between the feature and the target variable without analyzing the actual data.