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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.

URL: Data https://archive.ics.uci.edu/ml/machine-learning-databases/housing/

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 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. The data we used in this workflow is taken from https://archive.ics.uci.edu/ml/machine-learning-databases/housing/. Workflow Requirements KNIME Analytics Platform 3.7.0 or higher KNIME XGBoost Integration KNIME Optimization Extension KNIME JavaScript Views
The model should reach a R^2 value above 0.91 and a mean squared error below 8.
score predictions
Numeric Scorer (deprecated)
train model on 9 folds
XGBoost Tree Ensemble Learner (Regression) (deprecated)
predict test set
XGBoost Predictor (Regression)
10-fold cross validation
X-Partitioner
train model on full training set
XGBoost Tree Ensemble Learner (Regression) (deprecated)
predict the left out fold
XGBoost Predictor (Regression)
score prediction
X-Aggregator
Get Mean of MSE across Folds
visualize predictions
Scatter Plot (JavaScript) (legacy)
train / test 80 / 20
Table Partitioner
minimize the mean MSE
Parameter Optimization Loop End
get best Boosting Rounds configuration
Table Row to Variable (deprecated)
File Reader (deprecated)
Find best value for Boosting Rounds with Random Search
Parameter Optimization Loop Start

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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: 2026-02-04
Last update: 2026-02-04
Created with KNIME version: v5.9.0
Tags: XGBoostregressiongradient boostinggradient boosted treesparameter optimizationcross validationmachine learningdata mining

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