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Augmenting Data Beats Better Algorithms

Posted by kdawson on from the tell-it-to-the-dhs dept.

eldavojohn writes "A teacher is offering empirical evidence that when you're mining data, augmenting data is better than a better algorithm. He explains that he had teams in his class enter the Netflix challenge, and two teams went two different ways. One team used a better algorithm while the other harvested augmenting data on movies from the Internet Movie Database. And this team, which used a simpler algorithm, did much better — nearly as well as the best algorithm on the boards for the $1 million challenge. The teacher relates this back to Google's page ranking algorithm and presents a pretty convincing argument. What do you think? Will more data usually perform better than a better algorithm?"

3 of 179 comments (clear)

  1. Re:Is it just me that is surprised here? by gnick · · Score: 5, Informative

    The netflix challenge is to arrive at a better algorithm with the supplied data. Actually, the rules explicitly allow supplementing the data set and Netflix points out that they explore external data sets as well.
    --
    He's getting rather old, but he's a good mouse.
  2. Re:Heuristics?? by EvanED · · Score: 5, Informative

    One would hope that the thing that calculates the heuristic is an algorithm. See wikipedia.

  3. This does not mean what I think you think it means by aibob · · Score: 4, Informative

    I am a graduate student in computer science, emphasizing the use of machine learning.

    The sound bite conclusion of this blog post is that algorithms are a waste of time and that you are better off adding more training data.

    The reality is that a lot of really smart people have been trying to come up with better algorithms for classification, clustering, and (yes) ranking for a very long time. Unless you are already familiar with the field, you really are unlikely to invent something new that will work better than what is already out there.

    But that does not mean that the algorithm does not matter - for the problems I work on, using logistic regression or support vector machines outperforms naive bayes by 10% - 30%, which is huge. So if you want good performance, you try a few different algorithms to see what works.

    Adding more training data does not always help either, if the distributions of the data are significantly different. You are much better off using the data to design better features which represent/summarize the data.

    In other words, the algorithm is not unimportant, it just isn't the place your creative work is going to have the highest ROI.