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ensemble-learning's Introduction

Ensemble-Learning

机器学习算法之集成学习0807

  • 集成学习的概念
  • 个体学习器的概念
  • boosting bagging的概念、异同点
  • 理解不同的结合策略(平均法，投票法，学习法)
  • 随机森林的**
  • 随机森林的推广
  • 随机森林的优缺点
  • 随机森林在sklearn中的参数解释
  • 随机森林的应用场景

GBDT算法梳理 0808

  • 前向分布算法
  • 负梯度拟合
  • 损失函数
  • 回归
  • 二分类，多分类
  • 正则化
  • 优缺点
  • sklearn参数
  • 应用场景

XGBoost算法原理 0812

  • 损失函数
  • 分裂结点算法
  • 正则化
  • 对缺失值处理
  • 优缺点
  • 应用场景
  • sklearn参数

LightGBM算法梳理

  • LightGBM
  • LightGBM的起源
  • Histogram VS pre-sorted
  • leaf-wise VS level-wise
  • 特征并行和数据并行
  • 顺序访问梯度
  • 支持类别特征
  • 应用场景
  • sklearn参数
  • CatBoost(了解)

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Contributors

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Forkers

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