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

It is possible that education level can influence the decision to subscribe to a term deposit. Generally, individuals with higher education levels tend to have better financial literacy and may understand the benefits and risks associated with term deposits. Therefore, we can expect that individuals with higher education levels might be more likely to subscribe to a term deposit.

To create the dictionary, let's analyze the relationship between the feature and target variable by examining the distribution of the education levels for both classes (yes and no).

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

In this case, since the education feature has four possible categories, all categories are included in the dictionary for both target class "yes" and "no".