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18_​Decision_​Tree

18_Decision_Tree
Exercise Decision Tree1) Read data adult.csv2) Train a Decision Tree to predict whether or not a person earns more than 50k per year- Partition the dataset into a training set (75%) and a test set (25%). Apply stratified sampling option on the income column.- Train a Decision Tree model on the training set, and apply the model to the test setOPTIONAL: - Use the Scorer node to evaluate the accuracy of the model- Try out other parameter settings to get a higher accuracy. For example, change the quality measure, pruning method, orminimum number of records. Exercise Decision Tree1) Read data adult.csv2) Train a Decision Tree to predict whether or not a person earns more than 50k per year- Partition the dataset into a training set (75%) and a test set (25%). Apply stratified sampling option on the income column.- Train a Decision Tree model on the training set, and apply the model to the test setOPTIONAL: - Use the Scorer node to evaluate the accuracy of the model- Try out other parameter settings to get a higher accuracy. For example, change the quality measure, pruning method, orminimum number of records.

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2021-04-15
Last update: 2024-11-21
Created with KNIME version: v4.1.2
Tags: e-learningclassificationdecision treeeducation

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