In a recent video, I covered Random Forests and Neural Nets as part of the codecentric.ai Bootcamp. In the most recent video, I covered Gradient Boosting and XGBoost. You can find the video on YouTube and the slides on slides.com. Both are again in German with code examples in Python. But below, you find the English version of the content, plus code examples in R for caret, xgboost and h2o.
This is code that will encompany an article that will appear in a special edition of a German IT magazine. The article is about explaining black-box machine learning models. In that article I’m showcasing three practical examples: Explaining supervised classification models built on tabular data using caret and the iml package Explaining image classification models with keras and lime Explaining text classification models with xgboost and lime