alternate decisontree algorithms

  • Nobody/Anonymous

    I found that for building decision trees, several algorithms exist.

    Do you have one or several of them in waffles_learn ?

    Which specific algo is used in waffles_learn decisiontree ?

  • Nobody/Anonymous

    Ideally, we'd like to add switches so that the user could specify which
    technique is used. Currently, however, we only support two techniques: random
    splits, and splitting to maximize information gain like ID3, which is the
    default. Since ID3 doesn't really specify how continuous attributes are
    handled, we use an approach similar to C4.5 for those. Also, when missing
    values exist in the data, we replace those lazily with the most common value
    in the data--although I might change that to use a stochastically-selected
    value since that seems to yield better results in bagging ensembles. If you'd
    like to help us implement alternative methods of building the trees, that
    would be a welcome contribution.

  • Nobody/Anonymous

    I think the main reason I have not taken time to implement alternate decision
    tree algorithms is because I have not been convinced that they would be
    significantly better. Bagging, however, seems to improve accuracy with
    decision trees quite dramatically, so I think it may be a better use of time
    to work on improved ensemble techniques rather than to spend a lot of time
    fine-tuning the decision tree algorithm itself.



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