Based on prior knowledge, we can analyze the relationship between the feature "education level" and the target variable "subscription to a term deposit". 

Here is a possible analysis:

- "tertiary" education level: It is likely that clients with a higher level of education may have a higher probability of subscribing to a term deposit. Hence, it is possible that this category could have a higher proportion of "yes" values compared to other categories.
- "secondary" education level: Clients with a secondary education level may have a moderate probability of subscribing to a term deposit. 
- "unknown" education level: The relationship between an unknown education level and subscription to a term deposit is uncertain. This category could include clients with different levels of education, making it difficult to predict their tendency to subscribe.
- "primary" education level: Clients with a primary education level might have a lower probability of subscribing to a term deposit compared to those with higher education levels.

Based on this analysis, we can create a dictionary as follows:

```json
{
	"no": ["unknown", "primary"],
	"yes": ["tertiary", "secondary"]
}
```

Please note that this analysis is based on general assumptions, and the actual relationship between the feature and target variable may vary depending on the specific dataset and context.