Ever since I started migrating to data science I heard about the famous Bias versus Variance tradeoff.
But I learned it enough to move on with my studies and never looked back too much. I always knew that a highly biased model underfits the data, while a high-variance model is overfitted, and that any of those are not good when training an ML model.
I also know that we should look for a balance between both states, so we’ll have a good fit or a model that generalizes the pattern well to new data.
But I might say I never went farther than that. I never searched or created highly biased or highly variant models just to see what they actually do to the data and how the predictions of those models are.
That is until today, of course, because this is exactly what we’re doing in this post. Let’s proceed with some definitions.
