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update readme
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bensadeghi committed Jun 24, 2018
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[![DecisionTree](http://pkg.julialang.org/badges/DecisionTree_0.6.svg)](http://pkg.julialang.org/?pkg=DecisionTree&ver=0.6)
[![DecisionTree](http://pkg.julialang.org/badges/DecisionTree_0.7.svg)](http://pkg.julialang.org/?pkg=DecisionTree&ver=0.7)

Julia implementation of Decision Trees & Random Forests
Julia implementation of Decision Tree and Random Forest algorithms

## Classification
* pre-pruning (max depth, min leaf size)
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## Native API
### Classification Example
Pruned Tree Classifier
Decision Tree Classifier
```julia
# train full-tree classifier
model = build_tree(labels, features)
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Random Forest Classifier
```julia
# train random forest classifier
# using 2 random features, 10 trees, 0.5 portion of samples per tree (optional), and a maximum tree depth of 6 (optional)
# using 2 random features, 10 trees, 0.5 portion of samples per tree, and a maximum tree depth of 6
model = build_forest(labels, features, 2, 10, 0.5, 6)
# apply learned model
apply_forest(model, [5.9,3.0,5.1,1.9])
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Regression Random Forest
```julia
# train regression forest, using 2 random features, 10 trees,
# averaging of 5 samples per leaf (optional), and 0.7 portion of samples per tree (optional)
# averaging of 5 samples per leaf, and 0.7 portion of samples per tree
model = build_forest(labels, features, 2, 10, 5, 0.7)
# apply learned model
apply_forest(model, [-0.9,3.0,5.1,1.9,0.0])
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