A Python library that implements sklearn-compatible decision trees.
Check the examples directory to see how to use the package for both classification and regression problems.
- CART algorithm for building regression and classification trees
- Oblivious Trees
- Different splitting criteria: Gini index, entropy
Wait... Why would anyone want to implement his own DT package from scratch, while there's such thing as slklearn? That's some of the reasons:
- To better understand DTs and how they are constructed - there's no better way to understand something than to implement it;
- To have more freedom in trying different approaches in DTs construction (e.g. oblivious trees, etc). With the trees from sklearn one would have to code it in Cython, which is nasty.
- Implement tree pruning
- Add more parameters to tweak, make the interface more like
sklearn.tree.DecisionTree*classes - Speed things up, maybe switch to Cython?