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chore(README): explain how the top 10 method increases accuracy and F measure

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Mahdi Dibaiee 2016-08-21 01:21:42 +04:30
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@ -43,7 +43,7 @@ stack exec example-xor
# using Porter stemming, stopword elimination and a few custom techniques. # using Porter stemming, stopword elimination and a few custom techniques.
# The dataset is imbalanced which causes the classifier to be biased towards some classes (earn, acq, ...) # The dataset is imbalanced which causes the classifier to be biased towards some classes (earn, acq, ...)
# to workaround the imbalanced dataset problem, there is a --top-ten option which classifies only top 10 popular # to workaround the imbalanced dataset problem, there is a --top-ten option which classifies only top 10 popular
# classes, with evenly split datasets (100 for each) # classes, with evenly split datasets (100 for each), this increases F Measure significantly, along with ~10% of improved accuracy
# N-Grams don't seem to help us much here (or maybe my implementation is wrong!), using bigrams increases # N-Grams don't seem to help us much here (or maybe my implementation is wrong!), using bigrams increases
# accuracy, while decreasing F-Measure slightly. # accuracy, while decreasing F-Measure slightly.
stack exec example-naivebayes-doc-classifier -- --verbose stack exec example-naivebayes-doc-classifier -- --verbose