Variance** **measures the average value of the squared deviation from the mean. The instance with the *lowest *Variance is considered the *most *informative. Therefore, labeling this instance can be very useful to our model.

- If the Variance is
**0**, then the model doesn’t have a clue about the correct label. - If the Variance is
**1**, then the model has a clear “belief” about the correct label.

Imagine we have only 2 instances – A and B –, and the model has to decide which of them to suggest for annotation. It has made the following class predictions:

- Instance A: “cat” – 0.5, “milkshake” – 0.45, “cloud” – 0.05.
- Instance B: “cat” – 0.4, “milkshake” – 0.3, “cloud” – 0.3.

In this case, our model will choose instance B over A, as 0.0067 is less than 0.0406:

Learn more about the other heuristics: