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Evaluating Classifier Accuracy: Holdout & Cross-Validation Methods

  • Holdout method
    • Given data is randomly partitioned into two independent sets
      • Training set (e.g., 2/3) for model construction
      • Test set (e.g., 1/3) for accuracy estimation
    • Random sampling: a variation of holdout
      • Repeat holdout k times, accuracy = avg. of the accuracies obtained
  • Cross-validation (k-fold, where k = 10 is most popular)
    • Randomly partition the data into k mutually exclusive subsets, each approximately equal size
    • At i-th iteration, use Di as test set and others as training set
    • Leave-one-out: k folds where k = # of tuples, for small sized data
    • *Stratified cross-validation*: folds are stratified so that class dist. in each fold is approx. the same as that in the initial data

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