我在h2o中使用随机森林。 但是我不明白返回结果中参数的含义。 这是我的原始数据。
我希望看到这样的结果: (我将树的数量设置为3,响应列=“播放”。)
tree1:
Wind = false: yes {no=0, yes=6}
Wind = true
| Temperature > 77.500: no {no=2, yes=0}
| Temperature ≤ 77.500: yes {no=1, yes=5}
tree2:
Humidity > 92.500: no {no=3, yes=0}
Humidity ≤ 92.500: yes {no=2, yes=9}
tree3:
Wind = false: yes {no=0, yes=6}
Wind = true
| Temperature > 77.500: no {no=2, yes=0}
| Temperature ≤ 77.500: yes {no=1, yes=5}
但是我得到了一个包含很多参数但结果的模型。 这是我的代码和得到的结果:
DRFParametersV3 drfParams = new DRFParametersV3();
drfParams.trainingFrame = H2oApi.stringToFrameKey("train");
drfParams.validationFrame = H2oApi.stringToFrameKey("test");
drfParams.ntrees=3;
System.out.println("drfParams: " + drfParams);
ColSpecifierV3 responseColumn = new ColSpecifierV3();
responseColumn.columnName = ATT_LABEL_GOLF;
drfParams.responseColumn = responseColumn;
System.out.println("About to train DRF. . .");
DRFV3 drfBody = h2o.train_drf(drfParams);
System.out.println("drfParams: " + drfBody);
JobV3 job = h2o.waitForJobCompletion(drfBody.job.key);
System.out.println("DRF build done.");
ModelKeyV3 modelKey = (ModelKeyV3)job.dest;
ModelsV3 models = h2o.model(modelKey);
System.out.println("models: " + models);
System.out.println("models'size: " + models.models.length);
DRFModelV3 model = (DRFModelV3)models.models[0];
System.out.println("new DRF model: " + model);
答案 0 :(得分:1)
一种选择是下载MOJO,将其加载并在MOJO对象上使用功能_computeGraph
。看看H2O github repo,以从代码中学习。
还请查看有关POJO和MOJO here
的文档以下一些其他代码可能会有所帮助:https://github.com/h2oai/h2o-3/blob/43f8ab952a69a8bc9484bd0ffac909b6e3e820ca/h2o-algos/src/test/java/hex/XValPredictionsCheck.java#L59-L69