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06_​Random_​Forest

06_Random_Forest
Exercise Random Forest1) Read letter-recognition.csv. This dataset was downloaded from UC Irvine Machine Learning Repository.Here, we have an image recognition problem. Each image contains an alphabet letter that is described by variousmeasures. Col0 contains the target class (the letter). All other input features are measures of the image.2) Train a Random Forest model to predict the alphabet letter in column Col0- Partition the dataset into a training set (80%) and a test set (20%). Perform stratified sampling on the target column.- Train a Random Forest model on the training set to predict values in the target column. Train 5 trees with minimum nodesize 2.- Apply the trained model to the test set- Evaluate the accuracy of the model by scoring metrics for a classification model3) OPTIONAL: train a Random Forest with 100 trees, and compare the performance Node 1Top: train set (80%)Bottom: test set (20%)Stratified samplingon target column CSV Reader Random ForestLearner Random ForestPredictor Partitioning Random ForestLearner Scorer (JavaScript) Random ForestPredictor Scorer (JavaScript) Concatenate Exercise Random Forest1) Read letter-recognition.csv. This dataset was downloaded from UC Irvine Machine Learning Repository.Here, we have an image recognition problem. Each image contains an alphabet letter that is described by variousmeasures. Col0 contains the target class (the letter). All other input features are measures of the image.2) Train a Random Forest model to predict the alphabet letter in column Col0- Partition the dataset into a training set (80%) and a test set (20%). Perform stratified sampling on the target column.- Train a Random Forest model on the training set to predict values in the target column. Train 5 trees with minimum nodesize 2.- Apply the trained model to the test set- Evaluate the accuracy of the model by scoring metrics for a classification model3) OPTIONAL: train a Random Forest with 100 trees, and compare the performance Node 1Top: train set (80%)Bottom: test set (20%)Stratified samplingon target columnCSV Reader Random ForestLearner Random ForestPredictor Partitioning Random ForestLearner Scorer (JavaScript) Random ForestPredictor Scorer (JavaScript) Concatenate

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2022-03-12
Last update: 2024-05-01
Created with KNIME version: v4.5.1
Tags: e-learningclassificationtree ensembleRandom Foresteducation

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