26 lines
1.1 KiB
Markdown
26 lines
1.1 KiB
Markdown
sibe
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====
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A simple Machine Learning library.
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notMNIST dataset, cross-entropy loss, learning rate decay and sgd:
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See examples:
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```
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# neural network examples
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stack exec example-xor
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stack exec example-424
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# notMNIST dataset, achieves ~87% accuracy using exponential learning rate decay
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stack exec example-notmnist
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# Naive Bayes document classifier, using Reuters dataset
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# using Porter stemming, stopword elimination and a few custom techniques.
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# The dataset is imbalanced which causes the classifier to be biased towards some classes (earn, acq, ...)
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# to workaround the imbalanced dataset problem, there is a --top-ten option which classifies only top 10 popular
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# classes, with evenly split datasets (100 for each), this increases F Measure significantly, along with ~10% of improved accuracy
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# N-Grams don't seem to help us much here (or maybe my implementation is wrong!), using bigrams increases
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# accuracy, while decreasing F-Measure slightly.
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stack exec example-naivebayes-doc-classifier -- --verbose
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stack exec example-naivebayes-doc-classifier -- --verbose --top-ten
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```
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