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Extreme Gradient Boosting has a very efficient implementation. Unlike SVM and RandomForest, we can tune parameter using the whole downsampling set. We focus on varying Ridge & Lasso regularization and learning rate. We use 10% of data for validating tuning parameter.
1set.seed(400)
2#down sampling again so than we get more info when stacking
3samp = downSample(data_train[-getIndexsOfColumns(data_train, c( "loan_status") )],data_train$loan_status,yname="loan_status")
4#choose small data for validating
5train_index_tuning= createDataPartition(samp$loan_status,p = 0.1,list=FALSE,times=1)
6