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basic doc
Signed-off-by: Manish Amde <[email protected]>
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This package contains the default implementation of the decision tree algorithm.
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The decision tree algorithm supports:
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+ information loss calculation with entropy and gini for classification and variance for regression
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+ node model pruning
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+ printing to dot files
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+ unit tests
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+ Binary classification
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+ Regression
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+ Information loss calculation with entropy and gini for classification and variance for regression
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+ Both continuous and categorical features
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#Performance testing
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# Tree improvements
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+ Node model pruning
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+ Printing to dot files
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#Future Extensions
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# Future Ensemble Extensions
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+ Random forests
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+ Boosting
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+ Extremely randomized trees
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+ Extremely randomized trees

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