Hands-On Artificial Intelligence for IoT

Classification in H20

The same models can be used for classification in H2O, with only one change; we will need to change the output features from numeric values to categorical values using the asfactor() function. We will perform the classification on the quality of red wine, and use our old red wine database (Chapter 3, Machine Learning for IoT). We will need to import the same modules and initiate the H2O server. The full code is available at in the Chapter08/wine_classification_h2o.ipynb file:

1. Here is the code to import the necessary modules and initiate the H2O server:

import h2oimport timeimport seabornimport itertoolsimport numpy as npimport pandas as pdimport seaborn as snsimport matplotlib.pyplot as pltfrom h2o.estimators.glm import ...