A probabilistic gradient boosting framework in Python
PGBM Probabilistic Gradient Boosting Machines (PGBM) is a probabilistic gradient boosting framework in Python based on PyTorch, developed by Airlab in Amsterdam. It provides the following advantages over existing frameworks: Probabilistic regression estimates instead of only point estimates. Auto-differentiation of custom loss functions. Native GPU-acceleration. It is aimed at users interested in solving large-scale tabular probabilistic regression problems, such as probabilistic time series forecasting. For more details, read our paper or check out the examples. Installation Run pip install pgbm […]
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