R Deep Learning Cookbook

How it works...

The following is the performance of the grid-searched model on both the training and cross-validation datasets. We can observe that the AUC has increased by one unit in both training and cross-validation scenarios, after performing a grid search. The training and cross validation AUC after the grid search is 0.996 and 0.997 respectively.

# Performance on Training data after grid search> train_performance.grid <- h2o.performance(best_dl_model,train = T)> train_performance.grid@metrics$AUC[1] 0.9965881# Performance on Cross validation data after grid search> xval_performance.grid <- h2o.performance(best_dl_model,xval = T)> xval_performance.grid@metrics$AUC[1] 0.9979131

Now, let's assess the performance of the best grid-searched ...