构建 h2oensemble 模型时出现 NullPointerException 错误
NullPointerException error while building h2oensemble model
我正在尝试构建 3 个模型的集合,即......深度学习、随机森林和梯度提升。我已将模型 ID 作为列表传递给集成函数,但出现以下错误:
java.lang.NullPointerException
java.lang.NullPointerException
at hex.StackedEnsembleModel.checkAndInheritModelProperties(StackedEnsembleModel.java:258)
at hex.ensemble.StackedEnsemble$StackedEnsembleDriver.computeImpl(StackedEnsemble.java:116)
at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:169)
at water.H2O$H2OCountedCompleter.compute(H2O.java:1241)
at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)
Error: java.lang.NullPointerException
这是我对集成模型的论点:
my_ensemble <- h2o.stackedEnsemble(x=2:length(names(train)),y=1,
training_frame = train,validation_frame = valid,
base_models = list(ann1@model_id,rf1@model_id,
gbm1@model_id),model_id = "my_ensemble_1")
请指教我哪里做错了。
注意:我正在尝试对多项式分类进行预测。
H2O 堆叠合奏 not yet support multinomial classification -- only regression and binary classification. This is noted in the Stacked Ensemble documentation。这就是它失败的原因。
我正在尝试构建 3 个模型的集合,即......深度学习、随机森林和梯度提升。我已将模型 ID 作为列表传递给集成函数,但出现以下错误:
java.lang.NullPointerException
java.lang.NullPointerException
at hex.StackedEnsembleModel.checkAndInheritModelProperties(StackedEnsembleModel.java:258)
at hex.ensemble.StackedEnsemble$StackedEnsembleDriver.computeImpl(StackedEnsemble.java:116)
at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:169)
at water.H2O$H2OCountedCompleter.compute(H2O.java:1241)
at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)
Error: java.lang.NullPointerException
这是我对集成模型的论点:
my_ensemble <- h2o.stackedEnsemble(x=2:length(names(train)),y=1,
training_frame = train,validation_frame = valid,
base_models = list(ann1@model_id,rf1@model_id,
gbm1@model_id),model_id = "my_ensemble_1")
请指教我哪里做错了。
注意:我正在尝试对多项式分类进行预测。
H2O 堆叠合奏 not yet support multinomial classification -- only regression and binary classification. This is noted in the Stacked Ensemble documentation。这就是它失败的原因。