To analyze the relationship between the `native-country` feature and the target variable, we can start by looking at the distribution of `native-country` values for each target class.

Let's examine the distribution of `native-country` values for individuals who earn more than 50000 dollars per year (target class "yes"):

- United-States, Germany, Philippines, India, Canada, England, Taiwan, Italy, China, Japan, Yugoslavia, Hong, Greece, France, Holand-Netherlands

Now, let's examine the distribution of `native-country` values for individuals who earn less than or equal to 50000 dollars per year (target class "no"):

- United-States, ?, Peru, Guatemala, Mexico, Dominican-Republic, Ireland, Puerto-Rico, South, Columbia, Cambodia, Poland, Laos, Cuba, Thailand, Ecuador, Nicaragua, El-Salvador, Honduras, Iran, Scotland, Jamaica, Vietnam, Hungary, Outlying-US(Guam-USVI-etc), Trinadad&Tobago, Haiti, Portugal

Based on this analysis, we can create the following dictionary:

```json
{
	"no": ["?", "Peru", "Guatemala", "Mexico", "Dominican-Republic", "Ireland", "Puerto-Rico", "South", "Columbia", "Cambodia", "Poland", "Laos", "Cuba", "Thailand", "Ecuador", "Nicaragua", "El-Salvador", "Honduras", "Iran", "Scotland", "Jamaica", "Vietnam", "Hungary", "Outlying-US(Guam-USVI-etc)", "Trinadad&Tobago", "Haiti", "Portugal"],
	"yes": ["United-States", "Germany", "Philippines", "India", "Canada", "England", "Taiwan", "Italy", "China", "Japan", "Yugoslavia", "Hong", "Greece", "France", "Holand-Netherlands"]
}
```

Please note that the values in the dictionary are based on the available data and may not be exhaustive.