Bias vs Variance — The Mathematical Foundational Assessment of Models

Author(s): Kim Hyun Bin

Originally published on Towards AI.

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I am sure for many of you interested in the field of machine learning and artificial intelligence, when you first started, you would have come across this term “Bias and Variance trade off”. For the majority of us, I’m sure whoever was teaching us, be it YouTube, a Medium article or from school, have briefly explained about this from a conceptual point of view and highlighted the existence and importance of it. Sure, it is important and a foundational analysis of any statistical learning algorithm. But why and how did it come about and what makes it… what it is?

Maybe this thought have never crossed your mind and I don’t blame you for it because there are so much more fascinating and interesting algorithms and concepts out there, tempting you to just skip over the foundations and jump right into the complex algorithms. However, I am a true believer in polishing your foundations, building from the ground up and ensuring that you know exactly what is going on. I believe only then can you tackle more challenging concetps and equations in this insanely fast evolving field.

So today, I will… Read the full blog for free on Medium.

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Published via Towards AI