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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: | ||
+ information loss calculation with entropy and gini for classification and variance for regression | ||
+ node model pruning | ||
+ printing to dot files | ||
+ unit tests | ||
+ Binary classification | ||
+ Regression | ||
+ Information loss calculation with entropy and gini for classification and variance for regression | ||
+ Both continuous and categorical features | ||
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#Performance testing | ||
# Tree improvements | ||
+ Node model pruning | ||
+ Printing to dot files | ||
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#Future Extensions | ||
# Future Ensemble Extensions | ||
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+ Random forests | ||
+ Boosting | ||
+ Extremely randomized trees | ||
+ Extremely randomized trees |