Classification Threshold and Confusion Matrix

Using different thresholds for classification

So far, we have been using the trivial threshold of 50% to classify our data points, given the probabilities predicted by our model. Let us dive a bit deeper into this and see the effects of choosing different thresholds.

We will be working on the data points in the validation set. There are only 20 data points in it, so we can easily keep track of all of them.

First, let us compute the logits and corresponding probabilities: