# Variance

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.

<figure><img src="https://wiki.cloudfactory.com/media/pages/docs/userdocs/active-learning/active-learning-in-hasty/variance/3b989ba7a3-1684131886/koshki-v-stakane.webp" alt=""><figcaption><p>Source</p></figcaption></figure>

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:

<figure><img src="https://wiki.cloudfactory.com/media/pages/docs/userdocs/active-learning/active-learning-in-hasty/variance/9c26b0735b-1684131886/snimok-ekrana-1171.webp" alt=""><figcaption></figcaption></figure>


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