## An Introduction to Bias-Variance Tradeoff

I recently discussed model underfitting and overfitting. Essentially, these two concepts describe different ways that the model can fail to match your data set. Underfitting refers to making a model that’s not complex enough to accurately represent your data and misses trends in the data set. Overfitting refers to a situation where the model is too complex for the data set, and indicates trends in the data set that aren’t actually there.

Another way we can think about these topics is through the terms bias and variance. These two terms are fundamental concepts in data science and…