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As we can see,
the decision tree and SVM with radial basis function give comparable
performance in terms of the f measure, and both are much better than SVM with
linear kernel. (These are measures when the highest f measure is achieved
within each setting.) Also, In this particular setting, SVM with radial basis function gives
similar precision and recall, while decision tree achieves a better precision
but a worse recall.
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