Based on my prior knowledge, premature ventricular contractions (PVCs) are abnormal heartbeats that originate in the ventricles. PVCs can be categorized as either "no" (absence of PVCs in the anamnesis) or "yes" (presence of PVCs in the anamnesis).

To analyze the relationship between the feature nr_02 (PVCs in the anamnesis) and the occurrence of chronic heart failure, we need to examine the distribution of nr_02 values for each target class: "no" (no chronic heart failure) and "yes" (chronic heart failure).

Let's assume we have a dataset with the following observations:

Observation 1: nr_02 = "no", Target = "no"
Observation 2: nr_02 = "yes", Target = "no"
Observation 3: nr_02 = "yes", Target = "yes"
Observation 4: nr_02 = "yes", Target = "yes"
Observation 5: nr_02 = "no", Target = "yes"

Based on these observations, we can construct the dictionary as follows:

```json
{
	"no": ["no"],
	"yes": ["yes"]
}
```

The dictionary shows that for the target class "no" (no chronic heart failure), the feature nr_02 takes the value "no". Conversely, for the target class "yes" (chronic heart failure), the feature nr_02 takes the value "yes".

Please note that this is a simplified example, and in a real-world scenario, you would need a larger dataset to make more accurate conclusions.