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04 Decision Tree Model

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. 70% / 30%TravelNode 41Node 43 Data access andpreprocessing Partitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript) 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. 70% / 30%TravelNode 41Node 43Data access andpreprocessing Partitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript)

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Created by: Maarit Laukkanen
Created at: 2020-10-23
On NodePit since: 2023-02-10
Last update: 2024-11-23
Created with KNIME version: v4.7.0
Tags: educationself-pacedl2-dw

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