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
Used extensions & nodes
Created with KNIME Analytics Platform version 4.1.0
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