We used Google Colab to train our models. After we installed H2O in Google Colab, we initialized the H2O instance. We also imported the required libraries.
In order to use the H2O libraries, we imported H2OGeneralizedLinearEstimator, H2ORandomForestEstimator, and H2OGradientBoostingEstimator from h2o.estimators. We also imported H2OStackedEnsembleEstimator to train our model using a stacked ensemble.
We mounted Google Drive and read our dataset using h2o.import_file(). This created an H2O DataFrame, which is very similar to a pandas DataFrame. Instead of holding it in the memory, however, the data is located in one of the remote H2O clusters.
We then performed basic operations on the H2O DataFrame to analyze our data. We took ...
