```json
{
	"no": [1.0, 2.0, 3.0, 4.0, 5.0],
	"yes": [15.0, 16.0, 17.0, 18.0, 19.0]
}
```

Based on the feature description, "day" represents the last contact day of the month. To analyze the relationship between this feature and the task of whether the client subscribes to a term deposit or not, we can look at the possible values of "day" for each target class.

In this case, we are interested in finding 5 typical day values for each target class ('no' and 'yes'). Without knowing the specific dataset, we can't determine the exact values, but we can make an assumption based on prior knowledge.

For the 'no' class, clients who do not subscribe to a term deposit, the values of "day" might be distributed evenly throughout the month. Therefore, possible values for 'no' could include the first few days of the month, such as 1.0, 2.0, 3.0, 4.0, and 5.0.

On the other hand, for the 'yes' class, clients who subscribe to a term deposit, the values of "day" might be concentrated in the middle or towards the end of the month. Therefore, possible values for 'yes' could include mid-month or later, such as 15.0, 16.0, 17.0, 18.0, and 19.0.

These values are just hypothetical examples and might not represent the actual values in the dataset. However, they provide a general idea of how the feature 'day' could be related to the task of whether a client subscribes to a term deposit or not.