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 from a terminal within the virtual environment of your choice.

Verification

• Download & run an example from the examples folder to verify the installation is correct. Use both gpu and cpu as device to check if you are able to train

   

   

   

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