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10. Advanced Data Mining

Advanced Data Mining
Activity II: Parameter Optimization - Add a parameter optimization loop to your Random Forest model - Use Hillclimbing to determine the optimum number of models (min=10, max=200, step=10, int = yes) - Maximize the accuracy in the loop end node. - What were the optimal settings?(hint: don't forget to use the flow variable in your learner) Activity III: Cross Validation - Create a 10-fold cross validation for your Random Forest Learner. - Calculate the mean error for the cross validation. - Does the model seem stable? Activity I: Random Forest Model - Read the data file CurrentDetailData.table - Partition the data 50/50 using stratified sampling on the Target column - Create a Random Forest model to predict the Target column - Use a tree depth of 5, and 50 models. Node 1Node 2 Table Reader Random ForestLearner Activity II: Parameter Optimization - Add a parameter optimization loop to your Random Forest model - Use Hillclimbing to determine the optimum number of models (min=10, max=200, step=10, int = yes) - Maximize the accuracy in the loop end node. - What were the optimal settings?(hint: don't forget to use the flow variable in your learner) Activity III: Cross Validation - Create a 10-fold cross validation for your Random Forest Learner. - Calculate the mean error for the cross validation. - Does the model seem stable? Activity I: Random Forest Model - Read the data file CurrentDetailData.table - Partition the data 50/50 using stratified sampling on the Target column - Create a Random Forest model to predict the Target column - Use a tree depth of 5, and 50 models. Node 1Node 2 Table Reader Random ForestLearner

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Created by: knime
Created at: 2014-06-17
On NodePit since: 2019-08-19
Last update: 2020-03-23
Created with KNIME version: v4.0.0
Tags: tree ensembleclassificationparameter optimizationhillclimbingcross validationstable modeleducation

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