A few years ago I was sitting with a bunch of grad students in a data analytics class joking about R package names and ML terminology: e1071, partykit, confusion matrix, artifical nueral network, Perceptron, Belief Nets, Maximum a Posteriori. Take them of context and pretend you don’t know what they mean. (If you don’t, you’re probably a lot saner), and you’ll realize these words sound really cool. My vote for coolest name goes to Random Forest, but Confusion Matrix sounds awesome, too. My grad friends and I started nominating these for band names and song titles.

Perceptron comes out with it’s latest album, Random Forest, featuring its hit single Tangled in the Neural Net

Thus, I’m writing about Rademacher Complexity because of the cool title. Also, to further the liberation of machine learning knowledge from graduate school textbooks.

RC solves the problem of estimating over-fitting. In a PAC learning scenario, where we pick an Empirical Risk Minimizer $g$, our out of sample error can be thought of as how well we fit our training set plus a penalty on how much we overfit.

[1] $E_{out}(g) = E_{in}(g) + overfitpenalty$

The Rademacher penalty estimates the overfit penalty for classification tasks. For a more general estimate, see the permutation bound. The insight to the Rademacher penalty is that it estimates the overfit penalty by computing how much we overfit a dataset for which we can compute the optimism.

In a binary classification task, we copy the data and replace each original label with a label drawn from the “Rademacher Distribution,” a distribution where each label has an equal chance of being drawn $P (y_n =1/2)$. From there, we train the model on the data, minimize the in-sample error, and see how well we fit the noise. We call that error $E_r$.

What’s nice about the Rademacher penalty is that it is simple, intuitive, and data dependent. It accounts for how well the training set lends itself to over-fitting. That makes it tighter than the worst-case VC Bound.

These two bounds are related. For a constant $C$ and VC dimenion $d$, the Rademacher Complexity is bounded by

Other tools

Rademacher Complexity is a good tool for model selection and controlling over-fitting in classification scenarios. It’s great because it directly estimates $E_{out}$ without a test set.

There are some other approaches you could take to estimate $E_{out}$. If you did the same thing using normally distributed variables, it’s called the Gaussian Complexity.

You can also compute a similar estimate by permuting the labels of the training data. It works for regression as well as classification, and has a tighter bound because it takes the original output labels into account. It’s called the permutation estimate and has an associated bound [3].

Confusion Matrix’s, new music video Probably Approximately Correct goes viral.

1. Abu-Mostafa, Magdon-Ismail, Lin: Nov-2014, Page 30, e-chapter 9, Learning From Data.
2. Peter L. Bartlett, Shahar Mendelson (2002) Rademacher and Gaussian Complexities: Risk Bounds and Structural Results. Journal of Machine Learning Research 3 463-482
3. Magdon-Ismail, Permutation Complexity Bound on Out-Sample Error, http://www.cs.rpi.edu/~magdon/ps/conference/PermCompNIPS2010.pdf