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RAPIDS Forest Inference Library: Prediction at 100 Million Rows per Second
Random forests (RF) and gradient-boosted decision trees (GBDTs) have become workhorse models of applied machine learning. XGBoost and LightGBM, popular packages implementing GBDT models, consistently rank among the most commonly used tools by data scientists on the Kaggle platform. We see similar interest in forest-based models in industry, where... Read more
The Complete Guide to Decision Trees (part 2)
(See part 1 here.) Now you may ask yourself: how do DTs know which features to select and how to split the data? To understand that, we need to get into some details. All DTs perform basically the same task: they examine all the attributes of the dataset to... Read more
The Complete Guide to Decision Trees (part 1)
In the beginning, learning Machine Learning (ML) can be intimidating. Terms like “Gradient Descent”, “Latent Dirichlet Allocation” or “Convolutional Layer” can scare lots of people. But there are friendly ways of getting into the discipline, and I think starting with this guide to decision trees is a wise decision.... Read more