Machine Learning Crash Course: Part 4 - The Bias-Variance Dilemma · ML@B

So what does this have to do with machine learning? Well, it turns out that machine learning algorithms are not that much different from our friend Doge: they often run the risk of over-extrapolating or over-interpolating from the data that they are trained on. There is a very delicate balancing act when machine learning algorithms try to predict things. On the one hand, we want our algorithm to model the training data very closely, otherwise we'll miss relevant features and interesting trends. However, on the other hand we don't want our model to fit too closely, and risk over-interpreting every outlier and irregularity. The Fukushima power plant disaster is a devastating example of overfitting. When designing the power plant, engineers had to determine how often earthquakes would occur.