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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? Read AB_NYC_2019Predict priceR2 and error metricsNode 99Node 100Node 101 InteractiveData Cleaning File Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Partitioning Replace 0 price byneighborhood average Parameter OptimizationLoop Start ParameterOptimization Loop End 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_2019Predict priceR2 and error metricsNode 99Node 100Node 101InteractiveData Cleaning File Reader Random Forest Learner(Regression) Random Forest Predictor(Regression) Numeric Scorer Partitioning Replace 0 price byneighborhood average Parameter OptimizationLoop Start ParameterOptimization Loop End Table Columnto Variable

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Created by: Maarit Laukkanen
Created at: 2020-10-27
On NodePit since: 2021-07-03
Last update: 2024-12-17
Created with KNIME version: v4.3.2
Tags: parameter optimization

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