This example shows how to build an H2O GLM model for regression, predict new data and score the regression metrics for model evaluation. 1. Prepare: Load the carspeed data, import the resulting KNIME Table to H2O and partition the data for test and train set 30/70. 2. Learn: We learn the GBMGLM Model using the "H2O Generalized Linear Model Learner (Regression) using the default algorithm settings. 3. Predict: Make predictions on test data using the model. 4. Score: In order to evaluate our model, we asess the accuracy by scoring the predictions made on the test data.

This is a companion discussion topic for the original entry at https://kni.me/w/268BS6WA8ehl3EsD