Summary

In this chapter, we learned about some of the optional parameters that are available to us in H2O AutoML. We started by understanding what imbalanced classes in a dataset are and how they can cause trouble when training models. Then, we understood oversampling and undersampling, which we can use to tackle this. After that, we learned how H2O AutoML provides parameters for us to control the sampling techniques so that we can handle imbalanced classes in datasets.

After that, we understood another concept, called early stopping. We understood how overtraining can lead to an overfitted ML model that performs very poorly against unseen new data. We also learned that early stopping is a method that we can use to stop model training once we start noticing that the model has started overfitting by monitoring the performance of the model against the validation dataset. We then learned about the various parameters that H2O AutoML has that we can use to automatically stop model training...