ML之catboost：基于自定义数据集利用catboost 算法实现回归预测(训练采用CPU和GPU两种方式)

目录

基于自定义数据集利用catboost 算法实现回归预测(训练采用CPU和GPU两种方式)

输出结果

# T1、训练采用CPU

# T2、训练采用GPU

实现代码

基于自定义数据集利用catboost 算法实现回归预测(训练采用CPU和GPU两种方式)

输出结果

# T1、训练采用CPU

  

   

    

     

    
    

     

      0:	learn: 7.9608417	total: 50.2ms	remaining: 1.46s
     
    
   

    

     

    
    

     

      1:	learn: 7.7618206	total: 50.5ms	remaining: 707ms
     
    
   

    

     

    
    

     

      2:	learn: 7.5879985	total: 50.9ms	remaining: 458ms
     
    
   

    

     

    
    

     

      3:	learn: 7.4148674	total: 51.4ms	remaining: 334ms
     
    
   

    

     

    
    

     

      4:	learn: 7.2488388	total: 51.6ms	remaining: 258ms
     
    
   

    

     

    
    

     

      5:	learn: 7.0676178	total: 51.8ms	remaining: 207ms
     
    
   

    

     

    
    

     

      6:	learn: 6.8909273	total: 51.9ms	remaining: 171ms
     
    
   

    

     

    
    

     

      7:	learn: 6.7186541	total: 52.1ms	remaining: 143ms
     
    
   

    

     

    
    

     

      8:	learn: 6.5506878	total: 52.3ms	remaining: 122ms
     
    
   

    

     

    
    

     

      9:	learn: 6.3869206	total: 52.4ms	remaining: 105ms
     
    
   

    

     

    
    

     

      10:	learn: 6.2272476	total: 52.6ms	remaining: 90.9ms
     
    
   

    

     

    
    

     

      11:	learn: 6.0715664	total: 52.8ms	remaining: 79.2ms
     
    
   

    

     

    
    

     

      12:	learn: 5.9197772	total: 52.9ms	remaining: 69.2ms
     
    
   

    

     

    
    

     

      13:	learn: 5.7717828	total: 53.1ms	remaining: 60.7ms
     
    
   

    

     

    
    

     

      14:	learn: 5.6274882	total: 53.2ms	remaining: 53.2ms
     
    
   

    

     

    
    

     

      15:	learn: 5.4868010	total: 53.4ms	remaining: 46.7ms
     
    
   

    

     

    
    

     

      16:	learn: 5.3496310	total: 53.6ms	remaining: 41ms
     
    
   

    

     

    
    

     

      17:	learn: 5.2158902	total: 53.7ms	remaining: 35.8ms
     
    
   

    

     

    
    

     

      18:	learn: 5.0854930	total: 53.9ms	remaining: 31.2ms
     
    
   

    

     

    
    

     

      19:	learn: 4.9583556	total: 54ms	remaining: 27ms
     
    
   

    

     

    
    

     

      20:	learn: 4.8343967	total: 54.2ms	remaining: 23.2ms
     
    
   

    

     

    
    

     

      21:	learn: 4.7135368	total: 54.4ms	remaining: 19.8ms
     
    
   

    

     

    
    

     

      22:	learn: 4.5956984	total: 54.5ms	remaining: 16.6ms
     
    
   

    

     

    
    

     

      23:	learn: 4.4808059	total: 54.7ms	remaining: 13.7ms
     
    
   

    

     

    
    

     

      24:	learn: 4.3687858	total: 54.9ms	remaining: 11ms
     
    
   

    

     

    
    

     

      25:	learn: 4.2595661	total: 55ms	remaining: 8.46ms
     
    
   

    

     

    
    

     

      26:	learn: 4.1530770	total: 55.2ms	remaining: 6.13ms
     
    
   

    

     

    
    

     

      27:	learn: 4.0492501	total: 55.3ms	remaining: 3.95ms
     
    
   

    

     

    
    

     

      28:	learn: 3.9480188	total: 55.5ms	remaining: 1.91ms
     
    
   

    

     

    
    

     

      29:	learn: 3.8493183	total: 55.7ms	remaining: 0us
     
    
   

    

     

    
    

     

      [14.72696421 19.90303684]
     
    
  
 

  
 
 

# T2、训练采用GPU

  

   

    

     

    
    

     

      0:	learn: 7.9608417	total: 114ms	remaining: 3.29s
     
    
   

    

     

    
    

     

      1:	learn: 7.7618210	total: 118ms	remaining: 1.65s
     
    
   

    

     

    
    

     

      2:	learn: 7.5677758	total: 122ms	remaining: 1.1s
     
    
   

    

     

    
    

     

      3:	learn: 7.3785819	total: 125ms	remaining: 814ms
     
    
   

    

     

    
    

     

      4:	learn: 7.1941178	total: 128ms	remaining: 642ms
     
    
   

    

     

    
    

     

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      6:	learn: 6.8389078	total: 136ms	remaining: 446ms
     
    
   

    

     

    
    

     

      7:	learn: 6.6679348	total: 140ms	remaining: 385ms
     
    
   

    

     

    
    

     

      8:	learn: 6.5012368	total: 143ms	remaining: 335ms
     
    
   

    

     

    
    

     

      9:	learn: 6.3387062	total: 149ms	remaining: 297ms
     
    
   

    

     

    
    

     

      10:	learn: 6.1802387	total: 153ms	remaining: 264ms
     
    
   

    

     

    
    

     

      11:	learn: 6.0257332	total: 156ms	remaining: 234ms
     
    
   

    

     

    
    

     

      12:	learn: 5.8750898	total: 162ms	remaining: 212ms
     
    
   

    

     

    
    

     

      13:	learn: 5.7282121	total: 166ms	remaining: 190ms
     
    
   

    

     

    
    

     

      14:	learn: 5.5850064	total: 171ms	remaining: 171ms
     
    
   

    

     

    
    

     

      15:	learn: 5.4453814	total: 175ms	remaining: 153ms
     
    
   

    

     

    
    

     

      16:	learn: 5.3092462	total: 180ms	remaining: 137ms
     
    
   

    

     

    
    

     

      17:	learn: 5.1765153	total: 184ms	remaining: 122ms
     
    
   

    

     

    
    

     

      18:	learn: 5.0471030	total: 188ms	remaining: 109ms
     
    
   

    

     

    
    

     

      19:	learn: 4.9209255	total: 193ms	remaining: 96.3ms
     
    
   

    

     

    
    

     

      20:	learn: 4.7979018	total: 196ms	remaining: 84.2ms
     
    
   

    

     

    
    

     

      21:	learn: 4.6779541	total: 200ms	remaining: 72.7ms
     
    
   

    

     

    
    

     

      22:	learn: 4.5610056	total: 204ms	remaining: 62.1ms
     
    
   

    

     

    
    

     

      23:	learn: 4.4469804	total: 208ms	remaining: 51.9ms
     
    
   

    

     

    
    

     

      24:	learn: 4.3358058	total: 212ms	remaining: 42.5ms
     
    
   

    

     

    
    

     

      25:	learn: 4.2274105	total: 218ms	remaining: 33.6ms
     
    
   

    

     

    
    

     

      26:	learn: 4.1217256	total: 223ms	remaining: 24.8ms
     
    
   

    

     

    
    

     

      27:	learn: 4.0186824	total: 227ms	remaining: 16.2ms
     
    
   

    

     

    
    

     

      28:	learn: 3.9182151	total: 230ms	remaining: 7.95ms
     
    
   

    

     

    
    

     

      29:	learn: 3.8202597	total: 235ms	remaining: 0us
     
    
   

    

     

    
    

     

      [14.67884178 20.        ]
     
    
  
 

  
 
 

实现代码

  

   

    

     

    
    

     

      # ML之catboost：基于自定义数据集实现回归预测
     
    
   

    

     

    
    

     

      from catboost import CatBoostRegressor
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      #1、定义数据集
     
    
   

    

     

    
    

     

      train_data = [[12, 14, 16, 18], 
     
    
   

    

     

    
    

     

                    [23, 25, 27, 29], 
     
    
   

    

     

    
    

     

                    [32, 34, 36, 38]]
     
    
   

    

     

    
    

     

      train_labels = [10, 20, 30]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      eval_data = [[2, 4, 6, 8], 
     
    
   

    

     

    
    

     

                   [20, 21, 24, 33]]
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      #2、模型预测
     
    
   

    

     

    
    

     

      model_CatBR = CatBoostRegressor(iterations=30, learning_rate=0.1, depth=2)
     
    
   

    

     

    
    

     

      model_CatBR.fit(train_data, train_labels)
     
    
   

    

     

    
    

     

      preds = model_CatBR.predict(eval_data)
     
    
   

    

     

    
    

     

      print(preds)
     
    
  
 

  
 
 

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/115711121