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DTSTART:20210829T000000
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DTSTART:20211031T030000
RDATE:20221030T030000
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DTSTART:20220327T030000
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SUMMARY:Time Series Forecasting with scikit-learn's Quantile Gradient Boos
 ted Regression Trees - Olivier Grisel
DTSTART;TZID=Europe/Zurich:20220829T103000
DTEND;TZID=Europe/Zurich:20220829T120000
DTSTAMP:20260819T115253Z
UID:pretalx-euroscipy-2022-TXYGUK@pretalx.com
DESCRIPTION:This tutorial will introduce how to leverage scikit-learn's po
 werful\n**histogram-based gradient boosted regression trees** with various
  loss functions\n(Least squares\, **Poisson** and the **pinball loss** for
  quantile estimation) on a time\nseries forecasting problem. We will see h
 ow to leverage pandas to build **lag and\nwindowing features** and [scikit
 -learn](https://scikit-learn.org) time-series cross-validation tools and o
 ther\nmodel evaluation tools.
LOCATION:HS 120
URL:https://pretalx.com/euroscipy-2022/talk/TXYGUK/
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