H2O Crossvalidation

This workflow shows how to use cross-validation in H2O using the KNIME H2O Nodes. In the example we use the H2O Random Forest to predict the multiclass response of the IRIS data set using 5-folds and evaluate the cross-validated performance.

Customer prediction with H2O

The purpose of this workflow is to showcase the ease of use of the H2O functionalities from within KNIME. As a real world usecase we chose the "Restaurant Visitor Forecasting" competition on Kaggle.com: https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting

The workflow contains the following steps:
- Data preparation: Reading, cleaning, joining data and feature creation
- Creation of a local H2O context and transformation of a KNIME data table into an H2O frame

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