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JKISeason2-25

Workflow has two branches. The first employs parameter optimization and cross validation to produce optimized hyperparamters. The calculated paramters are fed to the second branch which predicts outcomes based on the best parameters.

Read DataTargetNode 5Node 6Node 7Node 8Node 9Node 10Node 11# Boosting RoundsMax Tree DepthetaNode 1570/30Node 17Node 18Node 19CSV Reader Number To String XGBoost Predictor Scorer XGBoost TreeEnsemble Learner Parameter OptimizationLoop Start ParameterOptimization Loop End X-Aggregator X-Partitioner Table Rowto Variable Table Rowto Variable Table Rowto Variable Merge Variables Partitioning XGBoost TreeEnsemble Learner XGBoost Predictor Scorer Read DataTargetNode 5Node 6Node 7Node 8Node 9Node 10Node 11# Boosting RoundsMax Tree DepthetaNode 1570/30Node 17Node 18Node 19CSV Reader Number To String XGBoost Predictor Scorer XGBoost TreeEnsemble Learner Parameter OptimizationLoop Start ParameterOptimization Loop End X-Aggregator X-Partitioner Table Rowto Variable Table Rowto Variable Table Rowto Variable Merge Variables Partitioning XGBoost TreeEnsemble Learner XGBoost Predictor Scorer

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

Download Workflow
Created by: genef
Created at: 2023-09-13
On NodePit since: 2023-09-15
Last update: 2026-01-06
Created with KNIME version: v4.7.7
Tags: JKISeason2-25

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