如何使用Python在H2o库中的GBM中重用cross_validation_fold_assignment() [英] How to reuse cross_validation_fold_assignment() with GBM in H2o library with Python

问题描述

我使用H2o库运行模型.我进行了5折交叉验证.

I run my model with H2o library. I run with 5 folds cross-validation.

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()

我得到了交叉验证折页作业.我尝试将其重用于具有其他参数(例如ntrees或Stopping_rounds)的新模型，但是我没有在文档中找到它.

I got the cross-validation fold assignment. I try to reuse it for a new model with other parameters such as ntrees or stopping_rounds, but I did not find it in the documents.

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

推荐答案

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))

这篇关于如何使用Python在H2o库中的GBM中重用cross_validation_fold_assignment()的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！