  ControlBurn is a Python package to construct feature-sparse tree ensembles
that support nonlinear feature selection and interpretable machine learning.
The algorithms in this package first build large tree ensembles that prioritize
basis functions with few features and then select a feature-sparse subset of
these basis functions using a weighted lasso optimization criterion. The
package includes visualizations to analyze the features selected by the
ensemble and their impact on predictions. Hence ControlBurn offers the accuracy
and flexibility of tree-ensemble models and the interpretability of sparse
generalized additive models.
  ControlBurn is scalable and flexible: for example, it can use warm-start
continuation to compute the regularization path (prediction error for any
number of selected features) for a dataset with tens of thousands of samples
and hundreds of features in seconds. For larger datasets, the runtime scales
linearly in the number of samples and features (up to a log factor), and the
package support acceleration using sketching. Moreover, the ControlBurn
framework accommodates feature costs, feature groupings, and $\ell_0$-based
regularizers. The package is user-friendly and open-source: its documentation
and source code appear on https://pypi.org/project/ControlBurn/ and
https://github.com/udellgroup/controlburn/.
