DTExtract is a tool for extracting model explanations in the form of decision trees. More precisely, given

• blackbox access to a model (i.e., for a given input, produce the corresponding output),
• a sampling distribution over the input space,

then DTExtract constructs a decision tree approximating that model.

0. Prerequisites
1. Setting Up DTExtract
2. Using DTExtract

DTExtract has been tested using Python 2.7. DTExtract depends on numpy, scipy, scikit-learn, and pandas.

Run setup.sh to set up the datasets used in the examples that come with DTExtract.

See python/dtextract/examples/iris.py for an example using a dataset from the UCI machine learning repository with the goal of classifying Iris flowers. The dataset is located at data/iris.zip (download link). To run this example, run

$ cd python
$ python -m dtextract.examples.iris

Similarly, see python/dtextract/examples/diabetes.py for an example using a diabetes readmissions dataset. The dataset is located at data/dataset_diabetes.zip (download link). To run this example, run

$ cd python
$ python -m dtextract.examples.diabetes

Finally, see python/dtextract/examples/wine.py for an example using a dataset from the UCI machine learning repository with the goal of classifying wines. The dataset is located at data/wine.zip (download link). To run this example, run

$ cd python
$ python -m dtextract.examples.wine