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Repeated training and testing

One way of evaluating is to use percentage splits and repeatedly train and test the classifier using different splits, as Ian Witten demonstrates.

You can evaluate a classifier by splitting the dataset randomly into training and testing parts; train it on the former and test it on the latter. Of course, different splits produce slightly different results. If you simply re-run Weka, it repeats the same split – but you can force it to make different splits by altering the random number generator’s “seed”. If you evaluate the classifier several times you can average the results – and calculate the standard deviation.

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Data Mining with Weka

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