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Clax_​Written_​Letters_​Random_​Forest

Classification of written letters with a Random Forest
Classification of written Letters with Random Forests of Decision TreesClass column is Col0. Input columns are measures of the image containing the letters.Dataset from UC Irvine Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Letter+Recognition One random forest with 5 trees. One random forest with 100 trees. Compare overall accuracy. Random Forest of 5 decision trees to predict feature Income on the adultdataset Random Forest of 100 decision trees to predict feature Income on the adultdataset 80% for training 20 % for testing5 dec treesmin node size = 2100 dec treesmin node size = 2Read the dataset Partitioning Random ForestLearner Random ForestPredictor Scorer (JavaScript) Random ForestLearner Random ForestPredictor Scorer (JavaScript) ComparePerformances File Reader Classification of written Letters with Random Forests of Decision TreesClass column is Col0. Input columns are measures of the image containing the letters.Dataset from UC Irvine Machine Learning Repository https://archive.ics.uci.edu/ml/datasets/Letter+Recognition One random forest with 5 trees. One random forest with 100 trees. Compare overall accuracy. Random Forest of 5 decision trees to predict feature Income on the adultdataset Random Forest of 100 decision trees to predict feature Income on the adultdataset 80% for training 20 % for testing5 dec treesmin node size = 2100 dec treesmin node size = 2Read the dataset Partitioning Random ForestLearner Random ForestPredictor Scorer (JavaScript) Random ForestLearner Random ForestPredictor Scorer (JavaScript) ComparePerformances File Reader

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Created by: kathrinmelcher
Created at: 2017-09-14
On NodePit since: 2021-06-28
Last update: 2024-12-17
Created with KNIME version: v4.0.0
Tags: classificationmachine learningpredictionanalyticsKNIMEdata sciencerandom forestbaggingensemble modelsletters

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