DecisionTree.jl

Decision Tree Classifier and Regressor in Julia

Implementation of the ID3 algorithm

Classifier

Includes:

post pruning (pessimistic pruning)
parallelized bagging (random forests)
adaptive boosting (decision stumps)
cross validation (n-fold)
support for mixed nominal and numerical data

Adapted from MILK: Machine Learning Toolkit

Regressor

Includes:

parallelized bagging (random forests)
cross validation (n-fold)
support for numerical features

Note that regression is implied if labels/targets are of type float

Installation

You can install DecisionTree.jl using Julia's package manager

Pkg.add("DecisionTree")

Classification Example

Load RDatasets and DecisionTree packages

using RDatasets
using DecisionTree

Separate Fisher's Iris dataset features and labels

iris = dataset("datasets", "iris")
# on Julia v0.2
features = matrix(iris[:, 1:4]);
labels = vector(iris[:, 5]);
# on Julia v0.3
features = array(iris[:, 1:4]);
labels = array(iris[:, 5]);

Pruned Tree Classifier

# train full-tree classifier
model = build_tree(labels, features)
# prune tree: merge leaves having >= 90% combined purity (default: 100%)
model = prune_tree(model, 0.9)
# pretty print of the tree, to a depth of 5 nodes (optional)
print_tree(model, 5)
# apply learned model
apply_tree(model, [5.9,3.0,5.1,1.9])
# run n-fold cross validation for pruned tree,
# using 90% purity threshold purning, and 3 CV folds
accuracy = nfoldCV_tree(labels, features, 0.9, 3)

Random Forest Classifier

# train random forest classifier
# using 2 random features, 10 trees, and 0.5 portion of samples per tree (optional)
model = build_forest(labels, features, 2, 10, 0.5)
# apply learned model
apply_forest(model, [5.9,3.0,5.1,1.9])
# run n-fold cross validation for forests
# using 2 random features, 10 trees, 3 folds and 0.5 of samples per tree (optional)
accuracy = nfoldCV_forest(labels, features, 2, 10, 3, 0.5)

Adaptive-Boosted Decision Stumps Classifier

# train adaptive-boosted stumps, using 7 iterations
model, coeffs = build_adaboost_stumps(labels, features, 7);
# apply learned model
apply_adaboost_stumps(model, coeffs, [5.9,3.0,5.1,1.9])
# run n-fold cross validation for boosted stumps, using 7 iterations and 3 folds
accuracy = nfoldCV_stumps(labels, features, 7, 3)

Regression Example

n, m = 10^3, 5 ;
features = randn(n, m);
weights = rand(-2:2, m);
labels = features * weights;

Regression Tree

# train regression tree, using an averaging of 5 samples per leaf (optional)
model = build_tree(labels, features, 5)
# apply learned model
apply_tree(model, [-0.9,3.0,5.1,1.9,0.0])
# run n-fold cross validation, using 3 folds, averaging of 5 samples per leaf (optional)
# returns array of coefficients of determination (R^2)
r2 = nfoldCV_tree(labels, features, 3, 5)

Regression Random Forest