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08 Parameter Optimization - Solution

08 Parameter Optimization - Solution
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? Read AB_NYC_2019dataPredict priceR2 and error metricsControlnr of modelsMinimize MAPE InteractiveData Cleaning CSV Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Parameter OptimizationLoop Start ParameterOptimization Loop End Partitioning Replace 0 price byneighborhood average Table Columnto Variable 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? Read AB_NYC_2019dataPredict priceR2 and error metricsControlnr of modelsMinimize MAPEInteractiveData Cleaning CSV Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Parameter OptimizationLoop Start ParameterOptimization Loop End Partitioning Replace 0 price byneighborhood average Table Columnto Variable

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Created by: Maarit Laukkanen
Created at: 2020-10-27
On NodePit since: 2021-07-21
Last update: 2024-12-23
Created with KNIME version: v4.4.0
Tags: parameter optimization

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