Based on my prior knowledge, marital status can potentially have an impact on whether a client subscribes to a term deposit or not. It is likely that married individuals may be more inclined to invest in a term deposit compared to single or divorced individuals. However, this is just a hypothesis and further analysis is required to confirm the relationship between marital status and the target variable.

To analyze the relationship between the feature "marital" and the task "Does this client subscribe to a term deposit?", we can calculate the percentage of clients who subscribed to a term deposit (yes) and who did not subscribe (no) for each category of marital status. Let's assume we have the following data:

```
Marital    Target
-----------------
married    yes
single     no
married    yes
divorced   no
single     yes
married    no
divorced   yes
single     no
```

Based on this data, we can calculate the following:

- For the category 'married', the target classes are 'yes' and 'no'.
- For the category 'single', the target classes are 'yes' and 'no'.
- For the category 'divorced', the target classes are 'no' and 'yes'.


```json
{
	"no": ["single", "divorced"],
	"yes": ["married", "single", "divorced"]
}
```

In this example, we see that individuals who are married and single have subscribed to a term deposit (yes) at different rates. Divorced individuals only have a single occurrence, so their effect on the outcome is uncertain. Based on this analysis, we can create a dictionary with the possible marital values for each target class:

```json
{
	"no": ["single", "divorced"],
	"yes": ["married", "single", "divorced"]
}
```

Please note that this analysis is based on a limited dataset, and it is recommended to perform a comprehensive analysis on a larger dataset to confirm the relationship between marital status and the target variable.