Procedure 6: Creating a Neural Network with R
Although all of the work is offloaded to H2O, the instruction to train a model looks a lot like previous examples where a variety of R packages have been used. In this example the deep-learning function of the H2O package is going to be used (this is really the only reason that we are using H2O in the first place).
In order to make the command easier to understand, typed parameters will be used as follows:
| Parameter | Description |
|---|---|
| x | c(“Count_Transactions_1_Day”,”Authenticated”,”Count_Transactions_PIN_Decline_1_Day”,”Count_Transactions_Declined_1_Day”,”Count_Unsafe_Terminals_1_Day”,”Count_In_Person_1_Day”,”Count_Internet_1_Day”,”ATM”,”Count_ATM_1_Day”,”Count_Over_30_SEK_1_Day”,”In_Person”,”Transaction_Amt”,”Sum_Transactions_1_Day”,”Sum_ATM_Transactions_1_Day”,”Foreign”,”Different_Country_Transactions_1_Week”,”Different_Merchant_Types_1_Week”,”Different_Decline_Reasons_1_Day”,”Different_Cities_1_Week”,”Count_Same_Merchant_Used_Before_1_Week”,”Has_Been_Abroad”,”Cash_Transaction”,”High_Risk_Country”) |
| y | c(“Dependent”) |
| training_frame | TrainingHex |
| validation_frame | CVHex |
| standardise | FALSE |
| activation | Rectifier |
| epochs | 50 |
| seed | 12345 |
| hidden | 5 |
| variable_importance | TRUE |
| nfolds | 5 |
| adaptive_rate | FALSE |
The deep-learning function in H2O takes a function two vectors that contain the dependent and independent variables. For readability, create these string vectors to be passed to the deep-learning function in advance, rather than use the c() function, inside the function call. To create a list of eligible independent variables for the purposes of this example, enter:
x <- c("Count_Transactions_1_Day","Authenticated","Count_Transactions_PIN_Decline_1_Day","Count_Transactions_Declined_1_Day","Count_Unsafe_Terminals_1_Day","Count_In_Person_1_Day","Count_Internet_1_Day","ATM","Count_ATM_1_Day","Count_Over_30_SEK_1_Day","In_Person","Transaction_Amt","Sum_Transactions_1_Day","Sum_ATM_Transactions_1_Day","Foreign","Different_Country_Transactions_1_Week","Different_Merchant_Types_1_Week","Different_Decline_Reasons_1_Day","Different_Cities_1_Week","Count_Same_Merchant_Used_Before_1_Week","Has_Been_Abroad","Cash_Transaction","High_Risk_Country")
Run the line of script to console:
To instruct H2O to begin deep learning, enter:
Model <- h2o.deeplearning(x=x, y="Dependent",training_frame=TrainingHex.hex,validation_frame=CVHex.hex,activation="Rectifier",epochs=50,seed=12345,hidden=5,variable_importance=TRUE,nfolds=5,adaptive_rate=FALSE,standardize=TRUE)
Feedback from the H2O cluster will be received, detailing training progress.