As we saw with ensemble methods, when we know the strengths and weaknesses of our model, we can employ strategies to attempt to improve performance. We may have two models to classify something, but they most likely won't agree on everything. However, say that we know that one does better on edge cases, while the other is better on the more common ones. In that case, we would likely want to investigate a voting classifier to improve our performance. How can we know how the models perform in different situations, though?
We can take a look at the probabilities the model predicts that an observation belongs to a given class. This can give us insight into how confident our model is when it is correct and when it errs. We can use our pandas data wrangling skills to make quick work of this. Let's see how confident our original...
