thomas-j-fan-deep-dive-into-scikit-learn-s-histgradientboosting-classifier-pydata-new-york-2019.json

{
  "copyright_text": null,
  "description": "Gradient boosting decision trees (GBDT) is a powerful machine-learning technique known for its high predictive power with heterogeneous data. In this talk, we will explore scikit-learn's implementation of histogram-based GBDT called HistGradientBoostingClassifier/Regressor and how it compares to other GBDT libraries such as XGBoost, CatBoost, and LightGBM.",
  "duration": 1932,
  "language": "eng",
  "recorded": "2019-11-05",
  "related_urls": [
    {
      "label": "Conference schedule",
      "url": "https://pydata.org/nyc2019/schedule/"
    },
    {
      "label": "talk repository",
      "url": "https://github.com/thomasjpfan/pydata-2019-histgradientboosting"
    }
  ],
  "speakers": [
    "Thomas J Fan"
  ],
  "tags": [],
  "thumbnail_url": "https://i.ytimg.com/vi/J9QQ6l_HToU/maxresdefault.jpg",
  "title": "Deep Dive into scikit-learn's HistGradientBoosting Classifier and Regressor",
  "videos": [
    {
      "type": "youtube",
      "url": "https://www.youtube.com/watch?v=J9QQ6l_HToU"
    }
  ]
}