Pedro Tabacof Presents:
Unlocking the Power of Gradient-Boosted Trees (using LightGBM)
Gradient-boosted trees (XGBoost, LightGBM, Catboost) have become the staple of machine learning for tabular datasets. While most data scientists have made use of them at some point, many don’t know the true power those Python libraries provide. I will take LightGBM as an example and show in practice how it handles missing value imputation and categorical encoding natively, the different loss functions it provides for different problems (including the creation of your own loss function!), and how to interpret the resulting models. My aim is to show how LightGBM is like a Swiss army knife for machine learning and why it is the most pragmatic choice for tabular problems.
Github: github.com/cat...
Slides: pydata.org/lon...
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00:00 Welcome!
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2 окт 2024
