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cross validation demo

Advanced Data Mining - Solution

Solution to the exercise 10 for KNIME User Training
- Training a Random Forest model to predict a nominal target column
- Evaluating the performance of a classification model
- Optimizing parameters of the Random Forest model
- Performing the classification multiple times in a cross validation loop

Part I: Random Forest Model - Read CurrentDetailData.table data - Partition the data 70/30 using stratified sampling on the "Target" column - Train and apply a Random Forest model to predict the "Target" column - Use a tree depth of 5 and 160 models Part II: Cross Validation - Create a 10-fold cross validation for your model - Take a look at the error rates produced by the different iterations. Does the model seem stable? X-Partitioner X-Aggregator Partitioning Random ForestLearner Random ForestPredictor Random ForestLearner Random ForestPredictor Scorer Table Reader Scorer Part I: Random Forest Model - Read CurrentDetailData.table data - Partition the data 70/30 using stratified sampling on the "Target" column - Train and apply a Random Forest model to predict the "Target" column - Use a tree depth of 5 and 160 models Part II: Cross Validation - Create a 10-fold cross validation for your model - Take a look at the error rates produced by the different iterations. Does the model seem stable? X-Partitioner X-Aggregator Partitioning Random ForestLearner Random ForestPredictor Random ForestLearner Random ForestPredictor Scorer Table Reader Scorer

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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: knime
Created at: 2014-06-17
On NodePit since: 2023-01-25
Last update: 2024-04-27
Created with KNIME version: v4.7.0
Tags: tree ensembleclassificationparameter optimizationhillclimbingcross validationstable modeleducation

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