David Andrzejewski at Bayes’ Cave wrote up a nice summary of practical machine learning advice from the KDD 2011 paper “Detecting Advesarial Advertisements in the Wild”.  I’ve quoted below several of the main points from David’s summary:

• ABE: Always Be Ensemble-ing
• Throw a ton of features at the model and let L1 sparsity figure it out
• Map features with the “hashing trick“
• Handle the class imbalance problem with ranking
• Use a cascade of classifiers
• make sure the system “still works” as its inputs evolve over time
• Make efficient use of expert effort
• Allow humans to hard-code rules
• periodically use non-expert evaluations to make sure the system is working

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