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02_​Housing_​Value_​Regression_​with_​XGBoost

Housing Value Prediction using XGBoost for Regression

This workflow shows how the XGBoost nodes can be used for regression tasks.
It also demonstrates a combination of parameter optimization with cross validation to find the optimal value for the number of boosting rounds.

Parameter optimization with cross validation to find best value for the boosting rounds parameter Train and score model with the full training dataset and the optimal value for boosting rounds Housing Value Prediction using XGBoost for RegressionThis workflow shows how the XGBoost nodes can be used for regression tasks.It also demonstrates a combination of parameter optimization with cross validation to find the optimal value for the number of boosting rounds.The data we used in this workflow is taken from https://archive.ics.uci.edu/ml/machine-learning-databases/housing/.Workflow RequirementsKNIME Analytics Platform 3.7.0 or higherKNIME XGBoost IntegrationKNIME Optimization ExtensionKNIME JavaScript Views The model should reach a R^2value above 0.91 and a meansquared error below 8. scorepredictionFind best valuefor Boosting Roundswith Random Searchminimizethe mean MSE10-fold cross validationpredict the leftout foldtrain model on9 foldsvisualize predictionstrain / test80 / 20get best Boosting Roundsconfigurationtrain modelon full training setscore predictionspredict testset File Reader X-Aggregator Parameter OptimizationLoop Start ParameterOptimization Loop End X-Partitioner XGBoost Predictor(Regression) XGBoost Tree EnsembleLearner (Regression) Scatter Plot Partitioning Table Row to Variable(deprecated) XGBoost Tree EnsembleLearner (Regression) Numeric Scorer(deprecated) XGBoost Predictor(Regression) Get Mean of MSEacross Folds Parameter optimization with cross validation to find best value for the boosting rounds parameter Train and score model with the full training dataset and the optimal value for boosting rounds Housing Value Prediction using XGBoost for RegressionThis workflow shows how the XGBoost nodes can be used for regression tasks.It also demonstrates a combination of parameter optimization with cross validation to find the optimal value for the number of boosting rounds.The data we used in this workflow is taken from https://archive.ics.uci.edu/ml/machine-learning-databases/housing/.Workflow RequirementsKNIME Analytics Platform 3.7.0 or higherKNIME XGBoost IntegrationKNIME Optimization ExtensionKNIME JavaScript Views The model should reach a R^2value above 0.91 and a meansquared error below 8. scorepredictionFind best valuefor Boosting Roundswith Random Searchminimizethe mean MSE10-fold cross validationpredict the leftout foldtrain model on9 foldsvisualize predictionstrain / test80 / 20get best Boosting Roundsconfigurationtrain modelon full training setscore predictionspredict testsetFile Reader X-Aggregator Parameter OptimizationLoop Start ParameterOptimization Loop End X-Partitioner XGBoost Predictor(Regression) XGBoost Tree EnsembleLearner (Regression) Scatter Plot Partitioning Table Row to Variable(deprecated) XGBoost Tree EnsembleLearner (Regression) Numeric Scorer(deprecated) XGBoost Predictor(Regression) Get Mean of MSEacross Folds

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

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Created by: Adrian Nembach
Created at: 2018-12-05
On NodePit since: 2019-07-12
Last update: 2024-04-25
Created with KNIME version: v4.1.0
Tags: XGBoost regression gradient boosting gradient boosted trees parameter optimization cross validation machine learning data mining

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