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04 Decision Tree Model LAB

04 Decision Tree Model - Exercise

This workflow shows a hands-on exercise in the L2-DW Introduction to KNIME Analytics Platform for Data Wranglers - Advanced course

Exercise: Train a Decision Tree ModelThe provided metanode contains a workflow that accesses and preprocesses demographics, geo coordinates, andtravel risk information in different countries/territories. Specifically, it creates a binomial column Travel with twovalues "Travel safely" and "Travel with care" based on the travel risk categories. Your task is to predict this travel riskcategory via the following steps:1. Partition the data into a training set (70%) and a test set (30%). Apply stratified sampling on the Travel column.2. Build a decision tree model to predict the travel risk category (Travel column). Apply the model to the test set.3. Evaluate the model's performance. Use the Scorer or Scorer (JavaScript) node. Partitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript) Data access andpreprocessing Exercise: Train a Decision Tree ModelThe provided metanode contains a workflow that accesses and preprocesses demographics, geo coordinates, andtravel risk information in different countries/territories. Specifically, it creates a binomial column Travel with twovalues "Travel safely" and "Travel with care" based on the travel risk categories. Your task is to predict this travel riskcategory via the following steps:1. Partition the data into a training set (70%) and a test set (30%). Apply stratified sampling on the Travel column.2. Build a decision tree model to predict the travel risk category (Travel column). Apply the model to the test set.3. Evaluate the model's performance. Use the Scorer or Scorer (JavaScript) node. Partitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript) Data access andpreprocessing

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Created by: Maarit Laukkanen
Created at: 2020-10-23
On NodePit since: 2023-02-13
Last update: 2024-05-01
Created with KNIME version: v4.7.0
Tags: educationself-pacedl2-dw

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