第二章：决策树

同学笔记

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  sinat_41475087 2020-02-13 19:39:01

  来源：Scikit-Learn中的决策树模型 查看详情

  sklearn决策树

  穷举搜素所有特征可能的分裂点，没有实现剪枝（需要用交叉验证选择最佳参数）

  分类：DecisionTreeClassifier

  回归：DecisonTreeRegressor

  criterion："gini","entropy",

  splitter:"best","random"

   

  max_features：int, float or {“auto”, “sqrt”, “log2”}, default=None

  The number of features to consider when looking for the best split:

  • If int, then consider max_features features at each split.

  • If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.

  • If “auto”, then max_features=sqrt(n_features).

  • If “sqrt”, then max_features=sqrt(n_features).

  • If “log2”, then max_features=log2(n_features).

  • If None, then max_features=n_features

   

  以下限制模型复杂度的参数：

  max_depth:int

  max_leaf_nodes:int

  min_samples_split：int or float, default=2

  The minimum number of samples required to split an internal node

   

  min_samples_leaf：int or float, default=1

  The minimum number of samples required to be at a leaf node. 

   

  min_impurity_decrease：float, default=0.0

  A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

• 

  goodluckyangH 2020-02-04 20:20:23

  来源：Scikit-Learn中的决策树模型 查看详情

  分类决策树

  回归决策树

没有更多了