Decision Tree Extraction (DTExtract)
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.
Table of Contents
- Setting Up DTExtract
- Using DTExtract
DTExtract has been tested using Python 2.7. DTExtract depends on numpy, scipy, scikit-learn, and pandas.
Setting Up DTExtract
setup.sh to set up the datasets used in the examples that come with DTExtract.
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
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
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