This workflow shows how to transfer data from KNIME to H2O and vice versa.
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.
This workflow explains how to train a GBM classifier in H2O, predict classes of new data and evaluate the performance.
This example shows how to evaluate the performance of H2O classification (binominal and multinominal) and regression models.
This workflow shows how to use Parameter Optimization in combination with H2O. In the example we train multiple GBM models using brute force grid search and use the optimal parameters to train the final model.
This example shows how to build an H2O GLM model for regression, predict new data and score the regression metrics for model evaluation.
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