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08 Parameter Optimization

08 Parameter Optimization
Exercise: Parameter Optimization LoopOptimize the number of trees in a Random Forest model.1.1) Start with a Parameter Optimization Loop Start node. Create aparameter for the number of trees with start value=50, end value=150, andincrement=10. It is an integer.1.2) Overwrite the number of models setting in the Random Forest Learner(Regression) node with this parameter1.3) Transform the numeric scoring metrics into flow variables. Use theTable Column to Variable node.1.4) End with a Parameter Optimization Loop End node. Use MAPE as theobjective value. What is the optimal number of trees? 80 Read AB_NYC_2019dataPredict priceR2 and error metrics# of tree modelsMAPE InteractiveData Cleaning CSV Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Partitioning Replace 0 price byneighborhood average Parameter OptimizationLoop Start Table Columnto Variable ParameterOptimization Loop End Exercise: Parameter Optimization LoopOptimize the number of trees in a Random Forest model.1.1) Start with a Parameter Optimization Loop Start node. Create aparameter for the number of trees with start value=50, end value=150, andincrement=10. It is an integer.1.2) Overwrite the number of models setting in the Random Forest Learner(Regression) node with this parameter1.3) Transform the numeric scoring metrics into flow variables. Use theTable Column to Variable node.1.4) End with a Parameter Optimization Loop End node. Use MAPE as theobjective value. What is the optimal number of trees? 80 Read AB_NYC_2019dataPredict priceR2 and error metrics# of tree modelsMAPEInteractiveData Cleaning CSV Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Partitioning Replace 0 price byneighborhood average Parameter OptimizationLoop Start Table Columnto Variable ParameterOptimization Loop End

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Created by: Maarit Laukkanen
Created at: 2020-10-27
On NodePit since: 2022-03-11
Last update: 2024-11-18
Created with KNIME version: v4.5.1
Tags: parameter optimization

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