尝试使用在模型预测中具有最高重要性的石灰来获取我的h2o GBM模型中的前3列。
答案 0 :(得分:1)
在开源h2o-3中还没有开箱即用的解决方案,但是有很多例子说明了如何实现这一点。这是回购/笔记本:
https://github.com/jphall663/interpretable_machine_learning_with_python / https://github.com/jphall663/interpretable_machine_learning_with_python/blob/master/lime.ipynb
https://github.com/h2oai/mli-resources / https://github.com/h2oai/mli-resources/blob/master/notebooks/lime.ipynb
https://content.oreilly.com/oriole/Interpretable-machine-learning-with-Python-XGBoost-and-H2O / https://content.oreilly.com/oriole/Interpretable-machine-learning-with-Python-XGBoost-and-H2O/blob/master/lime.ipynb
Marco Tulio的原始LIME套餐还有一些机会 工作:https://github.com/marcotcr/lime,请务必查看此示例:https://marcotcr.github.io/lime/tutorials/Tutorial_H2O_continuous_and_cat.html