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H2O Generalized Linear Model for regression

H2O Generalized Linear Model Machine learning Glm

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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

Used extensions & nodes

Created with KNIME Analytics Platform version 4.1.0
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    KNIME Core Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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    KNIME H2O Machine Learning Integration Trusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

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