如何在cross_validation_fold_assignment库中使用GBM重用cross_validation_fold_assignment()

Question

我使用H2o库运行我的模型。我运行了5倍交叉验证。

model = H2OGradientBoostingEstimator(
                        balance_classes=True, 
                        nfolds=5,
                        keep_cross_validation_fold_assignment=True,
                        seed=1234)
model.train(x=predictors,y=response,training_frame=data)
print('rmse: ',model.rmse(xval=True))
print('R2: ',model.r2(xval=True))
data_nfolds = model.cross_validation_fold_assignment()

我有交叉验证折叠作业。我尝试用ntree或stopping_rounds等其他参数将它重用为新模型，但在文档中没有找到它。

https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/keep_cross_validation_fold_assignment.html

Answer 1

我找到了答案。

nfolds_index = h2o.import_file('myfile_index.csv')
nfolds_index.set_names(["fold_numbers"])
data = data.cbind(nfolds_index)
model2 = H2OGradientBoostingEstimator( seed=1234)
model2.train(x=predictors,y=response,training_frame=data, fold_column="fold_numbers")
print('rmse: ',model2.rmse(xval=True))
print('R2: ',model2.r2(xval=True))