ML之DT：构建一个二元DT(sk的DTR)来进行评分预测+Graphviz可视化

ML之DT：构建一个二元DT(sk的DTR)来进行评分预测+Graphviz可视化

目录

Graphviz软件的下载

输出结果

Graphviz可视化

核心代码

Graphviz软件的下载

Graphviz：可视化工具Graphviz的简介、安装、使用方法、经典案例之详细攻略

输出结果

dot文件内容

      digraph Tree {
      node [shape=box] ;
      1 [label="X[10] <= 10.525\nmse = 0.652\nsamples = 1599\nvalue = 5.636"] ;
      2 [label="X[9] <= 0.575\nmse = 0.431\nsamples = 983\nvalue = 5.366"] ;
      1 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
      2 [label="X[1] <= 0.748\nmse = 0.328\nsamples = 391\nvalue = 5.151"] ;
      1 -> 2 ;
      3 [label="mse = 0.289\nsamples = 328\nvalue = 5.201"] ;
      2 -> 3 ;
      4 [label="mse = 0.448\nsamples = 63\nvalue = 4.889"] ;
      2 -> 4 ;
      5 [label="X[1] <= 0.405\nmse = 0.449\nsamples = 592\nvalue = 5.508"] ;
      1 -> 5 ;
      6 [label="mse = 0.486\nsamples = 144\nvalue = 5.833"] ;
      5 -> 6 ;
      7 [label="mse = 0.393\nsamples = 448\nvalue = 5.404"] ;
      5 -> 7 ;
      8 [label="X[9] <= 0.645\nmse = 0.702\nsamples = 616\nvalue = 6.067"] ;
      0 -> 8 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
      9 [label="X[1] <= 1.015\nmse = 0.705\nsamples = 272\nvalue = 5.728"] ;
      8 -> 9 ;
      10 [label="mse = 0.591\nsamples = 262\nvalue = 5.794"] ;
      9 -> 10 ;
      11 [label="mse = 0.6\nsamples = 10\nvalue = 4.0"] ;
      9 -> 11 ;
      12 [label="X[10] <= 11.55\nmse = 0.536\nsamples = 344\nvalue = 6.334"] ;
      8 -> 12 ;
      13 [label="mse = 0.495\nsamples = 206\nvalue = 6.121"] ;
      12 -> 13 ;
      14 [label="mse = 0.43\nsamples = 138\nvalue = 6.652"] ;
      12 -> 14 ;
      }
  
 

Graphviz可视化

评分预测的决策树

核心代码

      with open("wineTree.dot", 'w') as f:
       f = tree.export_graphviz(wineTree, out_file=f)
  
 

文章来源: yunyaniu.blog.csdn.net，作者：一个处女座的程序猿，版权归原作者所有，如需转载，请联系作者。

原文链接：yunyaniu.blog.csdn.net/article/details/85910927