With all the hype about deep learning and “AI“, it is not well publicized that for structured/tabular data widely encountered in business applications it is actually another machine learning algorithm, the gradient boosting machine (GBM) that most often achieves the highest accuracy in supervised learning/prediction tasks. In this talk we'll review some of the main open source GBM implementations such as xgboost, h2o, lightgbm, catboost, Spark MLlib (all of them available from R and Python) and we'll discus
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