ch07_拓展1_GBDT.ipynb

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    "梯度提升决策树（Gradient Boosting Decision Tree，GBDT）是一种基于boosting集成学习思想的加法模型，训练时采用前向分布算法进行贪婪的学习，每次迭代都学习一棵CART树来拟合之前 t-1 棵树的预测结果与训练样本真实值的残差。"
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    "知识范畴：\n",
    "- CART树、集成学习、梯度下降等GBDT梯度提升树模型的基础知识；\n",
    "- 提升树(Boosting Tree) 原理、提升树的例子、提升树的Python实现、残差、GBDT原理、GBDT的例子、GBDT的Sklearn实现、GBDT的可视化\n",
    "- GBDT模型的应用，比如特征组合，二分类、多分类等\n",
    "- GBDT模型进行总结，指出GBDT模型的优缺点，并与RF随机森林模型进行对比，并指出GBDT模型发展——XGBoost"
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    "# GBDT 基础"
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   "source": [
    "## CART"
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    "CART树可以分为回归树和决策树。GBDT中的树是CART回归树。\n",
    "\n"
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