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parameter_​optimization_​simple

Hyperparameters Optimization and Training a Random Forest

This workflow optimizes the hyperparameters of a random forest of decision trees and training it with the optimized hyperparameters.

Adult.csv dataset describes US census information. Output class is sex.

Random Forest of Decision Trees Hyperparameters OptimizationOptimizing hyperparameters of a random forest of decision trees.Adult.csv dataset describes US census information. Output class is sex. Hyperparameter optimization loop 80% for training 20 % for testingRead the datasetCreate tablewith the parametersto be optimizedPartitioning Parameter OptimizationLoop Start (Table) Random ForestLearner ParameterOptimization Loop End CSV Reader Random ForestPredictor Scorer Table Creator Random Forest of Decision Trees Hyperparameters OptimizationOptimizing hyperparameters of a random forest of decision trees.Adult.csv dataset describes US census information. Output class is sex. Hyperparameter optimization loop 80% for training 20 % for testingRead the datasetCreate tablewith the parametersto be optimizedPartitioning Parameter OptimizationLoop Start (Table) Random ForestLearner ParameterOptimization Loop End CSV Reader Random ForestPredictor Scorer Table Creator

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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: kathrinmelcher
Created at: 2017-09-14
On NodePit since: 2024-05-02
Last update: 2024-11-23
Created with KNIME version: v4.5.0
Tags: machine learningKNIMEdata sciencerandom foresthyperparameters optimization

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