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H2O Gradient Boosting Machine for classification

H2OMachine learningGradient Boosting MachineGbm
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Jul 10, 2017 8:00 AM
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This workflow explains how to train a GBM classifier in H2O, predict classes of new data and evaluate the performance. 1. Prepare: Load the IRIS data, import the resulting KNIME Table to H2O and partition the data for test and train set 30/70. 2. Learn: We learn the GBM Model using the H2O Gradient Boosting Machine Learner (Classification). We want H2O to build 1000 Trees using a multinominal distribution of the reponse, for it is a multilabel problem. All other model parameters are H2Os defaults. 3. Predict: Make predictions on new data using your model(s). In order to compute the Scoring metrics, we need to enable the "append individual class probabilities" parameter in the "H2O Predictor (Classification)" Node 4. Score: In order to evaluate our model, we asess the Classifiers accuracy by scoring the predictions made on the test data.

External resources

  • H2O GBM documentation
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Used extensions & nodes

Created with KNIME Analytics Platform version 4.2.3
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    KNIME CoreTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

    knime
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    KNIME H2O Machine Learning IntegrationTrusted extension

    KNIME AG, Zurich, Switzerland

    Version 4.1.0

    knime

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