python partial dependence plot toolbox
This repository is inspired by ICEbox. The goal is to visualize the impact of certain features towards model prediction for any supervised learning algorithm. (now support all scikit-learn algorithms)
The common headache
When using black box machine learning algorithms like random forest and boosting, it is hard to understand the relations between predictors and model outcome.
For example, in terms of random forest, all we get is the feature importance. Although we can know which feature is significantly influencing the outcome based on the importance calculation, it really sucks that we don’t know in which direction it is influencing. And in most of the real cases, the effect is non-monotonic.
We need some powerful tools to help understanding the complex relations between predictors and model prediction.
- Helper functions for visualizing target distribution as well as prediction distribution.
- Proper way to handle one-hot encoding features.
- Solution for handling complex mutual dependency among features.
- Support multi-class classifier.
- Support two variable interaction partial dependence plot.
through pip (latest stable version： 0.2.0)
$ pip install pdpbox
through git (latest develop version)
$ git clone https://github.com/SauceCat/PDPbox.git $ cd PDPbox $ python setup.py install
PDPbox can be tested using
$ pip install tox tox-venv
toxinside the pdpbox clone directory. This will run tests with python 2.7 and 3.6 (if available).
To test the documentation, call
tox -e docs. The documentation should open up in your browser if it is successfully build. Otherwise, the problem with the documentation will be reported in the output of the command.