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Neural networks are conceptually simpler, and easier to implement. The weaknesses of GPM regression are: 1.) the technique requires many hyperparameters such as the kernel function, and the kernel function chosen has many hyperparameters too, 2.) you must make several model assumptions, 3.) it usually doesn’t work well for extrapolation.Īn alternative to GPM regression is neural network regression. The strengths of GPM regression are: 1.) it works well with very few data points, 2.) you can feed the model apriori information if you know such information, 3.) the predicted values have confidence levels (which I don’t use in the demo). One of the reasons the GPM predictions are so close to the underlying generating function is that I didn’t include any noise/error such as the kind you’d get with real-life data. (Note: I included (0,0) as a source data point in the graph, for visualization, but that point wasn’t used when creating the GPM regression model.) But the model does not extrapolate well at all. The graph of the demo results show that the GPM regression model predicted the underlying generating function extremely well within the limits of the source data - so well you have to look closely to see any difference. The source data is based on f(x) = x * sin(x) which is a standard function for regression demos. For simplicity, and so that I could graph my demo, I used just one predictor variable.
#Gaussian software education code
I decided to refresh my memory of GPM regression by coding up a quick demo using the scikit-learn code library.įor my demo, the goal is to predict a single value by creating a model based on just six source data points.
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The technique is based on classical statistics and is very complicated. An example is predicting the annual income of a person based on their age, years of education, and height.Ī relatively rare technique for regression is called Gaussian Process Model. The goal of a regression problem is to predict a single numeric value.