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09 Decision Tree Model

09 Decision Tree and Classification Model Evaluation

Exercise for training a classification model.

Train and apply a decision tree model. Evaluate the model's performance with scoring metrics and an ROC curve.













Exercise: Decision Tree and Classification Model Evaluation1) Read the telco-customer-churn.csv file by executing the CSV Reader node2) Partition the dataset into a training set (75%) and a test set (25%). Apply stratified sampling to the churn column.3) Train a Decision Tree model on the training set to predict whether or not a person will churn.4) Apply the model to the test set5) Evaluate the accuracy of the model with scoring metrics- What is the overall accuracy of the model? 100%6) Open the configuration dialog of the Scorer (JavaScript) node and exclude those statistics from the class prediction statistics table that are also present in theconfusion matrix. Display the number of rows in the confusion matrix. - What is the number of rows in the test dataset? 1,7617) Evaluate the performance of the model with an ROC curve- What is the Area under the Curve statistics for the decision tree model? 0 telco-customer-churn.csvtraining set (75%) test set (25%)stratified sampling on income train the modelto predict incomeapply model to the test setscoring metricsconvert churn to binaryCSV Reader Partitioning DecisionTree Learner Decision TreePredictor MISSING Scorer(JavaScript) ROC Curve Rule Engine Exercise: Decision Tree and Classification Model Evaluation1) Read the telco-customer-churn.csv file by executing the CSV Reader node2) Partition the dataset into a training set (75%) and a test set (25%). Apply stratified sampling to the churn column.3) Train a Decision Tree model on the training set to predict whether or not a person will churn.4) Apply the model to the test set5) Evaluate the accuracy of the model with scoring metrics- What is the overall accuracy of the model? 100%6) Open the configuration dialog of the Scorer (JavaScript) node and exclude those statistics from the class prediction statistics table that are also present in theconfusion matrix. Display the number of rows in the confusion matrix. - What is the number of rows in the test dataset? 1,7617) Evaluate the performance of the model with an ROC curve- What is the Area under the Curve statistics for the decision tree model? 0 telco-customer-churn.csvtraining set (75%) test set (25%)stratified sampling on income train the modelto predict incomeapply model to the test setscoring metricsconvert churn to binaryCSV Reader Partitioning DecisionTree Learner Decision TreePredictor MISSING Scorer(JavaScript) ROC Curve Rule Engine

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2023-02-26
Last update: 2024-04-27
Created with KNIME version: v4.7.0
Tags: classificationdecision treeoverall accuracyscorerROC curveAuC

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