Sparse Forests with FIL

Introduction The RAPIDS Forest Inference Library, affectionately known as FIL, dramatically accelerates inference (prediction) for tree-based models, including gradient-boosted decision tree models (like those from XGBoost and LightGBM) and random forests. (For a deeper dive into the library overall, check out the original FIL blog.) Models in the original FIL are stored as dense binary … Continued...

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