Description
Boosting is a meta-learning approach that puts together an ensemble of weak classifiers in order to form a strong classifier.
Adaptive Boosting (Adaboost) is a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values).
icsiboost is a free and open-source implementation that allows for training from millions of examples by hundreds of features (or millions of sparse features) in a reasonable time/memory.
icsiboost provides a classification time code for c, python and java.
Detailed instructions on how to install and use the icsiboost utility on your Mac are available HERE.
icsiboost is cross-platform and it works on Mac OS X, Windows and Linux. Binaries for the Windows and Linux platforms are available on the project's homepage.
User Reviews for icsiboost FOR MAC 1
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for icsiboost FOR MAC
icsiboost for Mac is a powerful tool for meta-learning. Its Adaboost implementation is efficient and versatile, making it ideal for diverse classification tasks.