H2O Generalized Linear Model for regression

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.

External resources

• H2O GLM documentation