Peekaboo: Machine Learning Cheat Sheet (for scikit-learn)

As you hopefully have heard, we at scikit-learn are doing a user survey (which is still open by the way).
One of the requests there was to provide some sort of flow chart on how to do machine learning.

As this is clearly impossible, I went to work straight away.

This is the result:

Needless to say, this sheet is completely authoritative.

Thanks to Rob Zinkov for pointing out an error in one yes/no decision.

More seriously: this is actually my work flow / train of thoughts whenever I try to solve a new problem. Basically, start simple first. If this doesn't work out, try something more complicated.
The chart above includes the intersection of all algorithms that are in scikit-learn and the ones that I find most useful in practice.

Only that I always start out with "just looking". To make any of the algorithms actually work, you need to do the right preprocessing of your data - which is much more of an art than picking the right algorithm imho.

Anyhow, enjoy ;)
[edit2]
As many people are reading this now, I guess many of which are not familiar with scikit-learn, a clarification: with ensemble classifiers and ensemble regressors I mean random forests, extremely randomized trees, gradient boosted trees, and the soon-to-be-come weight boosted trees (adaboost).
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[edit]
As some people commented about structured prediction not being included in the chart:  There is SVMstruct, which is a great library and has interfaces to many languages, but is only free for non-comercial use.
There is also the library I'm working on, pystruct, which I will write about on another day ;)

The chart is not really comprehensive, as I focused on scikit-learn. Otherwise I certainly would have included neural networks ;)
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