11_​H2O_​AutoML_​Regression_​Model

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This example shows how to build a regression model with H2O AutoML, predict new data and score the regression metrics for model evaluation.

1. Prepare:
Load the Combined Cycle Power Plant data, import the resulting KNIME Table to H2O and partition the data for test and train set 20/80.

2. Learn:
We learn the regression model using the "H2O AutoML Learner (Regression)" node using the default algorithm settings but limiting the max runtime to 60 seconds. The node learns and optimizes multiple different models and selects automatically the best one. The node outputs are the selected model and a leaderboard showing the metrics for the different models. The first row of the leaderboard is the selected model.

3. Predict:
Make predictions on test data using the selected model.

4. Score:
In order to evaluate our model, we assess different metrics by scoring the predictions made on the test data.

Nodes

• Excel Reader1 ×
• H2O AutoML Learner (Regression)1 ×
• H2O Local Context1 ×
• H2O Regression Scorer1 ×
• H2O Partitioning1 ×
• Show all 7 nodes

Extensions

• FeatureKNIME Excel Support
• FeatureKNIME H2O Machine Learning Integration

Links

• Combined Cycle Power Plant data

  http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant
• H2O AutoML documentation

  docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html
• H2O AutoML Tutorial

  docs.h2o.ai/h2o-tutorials/latest-stable/h2o-world-2017/automl/index.html