Based on prior knowledge, we can analyze the relationship between the feature 'pdays' and the task of whether the client subscribes to a term deposit. 

In this case, the feature 'pdays' represents the number of days that have passed since the client was last contacted from a previous campaign. We can assume that if the value of 'pdays' is high, it indicates that the client was contacted a long time ago, whereas a low value indicates recent contact.

To analyze the relationship, we can consider typical values for each target class ('no' and 'yes'). Let's define a high value for 'pdays' as any value greater than 300, a low value as any value less than or equal to 10, and typical values within the range of 11 to 300.

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

```json
{
	"no": [900.0, 400.0, 800.0, 700.0, 600.0],
	"yes": [1.0, 3.0, 2.0, 4.0, 6.0]
}
```

In the dictionary above, the 'no' class includes typical values for 'pdays' that are considered high, such as 900.0, 400.0, 800.0, 700.0, and 600.0. On the other hand, the 'yes' class includes typical values for 'pdays' that are considered low, such as 1.0, 3.0, 2.0, 4.0, and 6.0.

Please note that these values are hypothetical and should be interpreted based on the specific data and context.