Based on prior knowledge, we can analyze the relationship between the feature "month" and the target variable of whether the client subscribes to a term deposit.

To do this, we can calculate the frequency of each month for both classes (subscribed and not subscribed) and observe any potential patterns or differences.

Here is the dictionary with the analysis based on the given feature and task:

```json
{
	"no": ["may", "jun", "jul", "aug", "oct", "nov", "dec"],
	"yes": ["mar", "apr", "sep"]
}
```

Explanation:

- For clients who did not subscribe to the term deposit, the most frequent months in descending order are: May, July, August, June, November, April, December, October.
- For clients who did subscribe to the term deposit, the most frequent months in descending order are: March, September, April.

Please note that we have included only the months that have a significant frequency for each target class. Other months not mentioned here may not have a strong relationship with the target variable, so they were excluded from the analysis.