WildWood is a python package providing improved random forest algorithms for multiclass classification and regression introduced in the paper Wildwood: a new random forest algorithm by S. Gaïffas, I. Merad and Y. Yu (2021). It follows scikit-learn's API and can be used as an inplace replacement for its Random Forest algorithms (although multilabel/multiclass training is not supported yet). WildWood mainly provides, compared to standard Random Forest algorithms, the following things:

• Improved predictions with less trees
• Faster training times (using a histogram strategy similar to LightGBM)
• Native support for categorical features
• Parallel training of the trees in the forest

Multi-class classification can be performed with WildWood using ForestClassifier while regression can be performed with ForestRegressor.

Documentation

Documentation is available here:

http://wildwood.readthedocs.io

Installation

The easiest way to install wildwood is using pip

pip install wildwood

But you can also use the latest development from github directly with

pip install git+https://github.com/pyensemble/wildwood.git

Basic usage

Basic usage follows the standard scikit-learn API. You can simply use

from wildwood import ForestClassifier

clf = ForestClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict_proba(X_test)[:, 1]

to train a classifier with all default hyper-parameters. However, let us pinpoint below some of the most interesting ones.

Categorical features

You should avoid one-hot encoding of categorical features and specify instead to WildWood which features should be considered as categorical. This is done using the categorical_features argument, which is either a boolean mask or an array of indices corresponding to the categorical features.

from wildwood import ForestClassifier

# Assuming columns 0 and 2 are categorical in X
clf = ForestClassifier(categorical_features=[0, 2])
clf.fit(X_train, y_train)
y_pred = clf.predict_proba(X_test)[:, 1]

For now, `WildWood` will actually use a maximum of 256 modalities for categorical 
features, since internally features are encoded using a memory efficient ``uint8`` data 
type. This will change in a near future.

Improved predictions through aggregation with exponential weights

By default (aggregation=True) the predictions produced by WildWood are an aggregation with exponential weights (computed on out-of-bag samples) of the predictions given by all the possible prunings of each tree. This is computed exactly and very efficiently, at a cost nearly similar to that of a standard Random Forest (which averages the prediction of leaves). See {ref}description-wildwood for a deeper description of WildWood.