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02_​H2O_​GBM_​Classification_​Model

H2O Gradient Boosting Machine for classification

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.

Training GBM Model for classification with H2O This tutorial shows how to train an H2O Model in KNIME.We will train a Gradient Boosting Machine ModelClassifier to predict the reponse class of the IRIS dataset(https://en.wikipedia.org/wiki/Iris_flower_data_set). 1. Prepare 2. Learn 4. Score 3. Predict Import IRIS data to H2O FramePartition data 30/70Start local H2O NodeLoad IRIS dataLearn the GBM with 1000 TreesPredict test datawith appended class probabilitiesMultinominal classification metrics and Confusion Matrix MISSING Tableto H2O MISSING H2OPartitioning MISSING H2OLocal Context Table Reader MISSING H2O GradientBoosting Machine Learner MISSING H2O Predictor(Classification) MISSING H2OMultinomial Scorer Training GBM Model for classification with H2O This tutorial shows how to train an H2O Model in KNIME.We will train a Gradient Boosting Machine ModelClassifier to predict the reponse class of the IRIS dataset(https://en.wikipedia.org/wiki/Iris_flower_data_set). 1. Prepare 2. Learn 4. Score 3. Predict Import IRIS data to H2O FramePartition data 30/70Start local H2O NodeLoad IRIS dataLearn the GBM with 1000 TreesPredict test datawith appended class probabilitiesMultinominal classification metrics and Confusion Matrix MISSING Tableto H2O MISSING H2OPartitioning MISSING H2OLocal Context Table Reader MISSING H2O GradientBoosting Machine Learner MISSING H2O Predictor(Classification) MISSING H2OMultinomial Scorer

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: nico
Created at: 2017-07-10
On NodePit since: 2020-11-23
Last update: 2024-12-21
Created with KNIME version: v4.2.3
Tags: H2Omachine learningGradient Boosting Machinegbm

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