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. They used a well-known law called the Gutenberg-Richter Law, which gives a way of predicting the probability of a very strong earthquake from the frequency that very weak earthquakes occur. This is useful because weak earthquakes–ones that are so weak that you can’t even feel them–happen almost all the time and are recorded by geologists, so the engineers had quite a large dataset to work with. Perhaps the most important result of this law is that the relationship between the magnitude of an earthquake and the logarithm of the probability that it happens is linear.
The engineers of the nuclear power plant used earthquake data from the past 400 years to train a regression model. Their prediction looked something like this:
In machine learning jargon, we call this overfitting. As the name implies, overfitting is when we train a predictive model that “hugs” the training data too closely. In this case, the engineers knew the relationship should have been a straight line but they used a more complex model than they needed to …
Continue reading: https://ml.berkeley.edu/blog/2017/07/13/tutorial-4