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LAB 2 Part 4

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 splitStratified sampling on theTravel columnPredict TravelEvaluate model accuracy 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. 70/30 splitStratified sampling on theTravel columnPredict TravelEvaluate model accuracyPartitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript) Data access andpreprocessing

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Created by: lisah
Created at: 2023-02-11
On NodePit since: 2023-02-12
Last update: 2024-05-01
Created with KNIME version: v4.7.0

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