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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 adult_joined.table file by executing the Table Reader node2) Partition the dataset into a training set (75%) and a test set (25%). Apply stratified sampling to the income column.3) Train a Decision Tree model on the training set to predict whether or not a person earns more than 50K per year4) Apply the model to the test set5) Evaluate the accuracy of the model with scoring metrics- What is the overall accuracy of the model?86%6) Open the configuration dialog of the Scorer (JavaScript) node and exclude those statistics from the classprediction statistics table that are also present in the confusion matrix. Display the number of rows in the confusionmatrix. - What is the number of rows in the test dataset?81937) Evaluate the performance of the model with an ROC curve- What is the Area under the Curve statistics for the decision tree model?.888) OPTIONAL: Try out other parameter settings to reach a better performance. For example, change the qualitymeasure, pruning method, or minimum number of records. Node 13Node 14Node 15Read adult_joined.tablescore Partitioning DecisionTree Learner Decision TreePredictor Table Reader Scorer (JavaScript) ROC Curve Exercise: Decision Tree and Classification Model Evaluation1) Read the adult_joined.table file by executing the Table Reader node2) Partition the dataset into a training set (75%) and a test set (25%). Apply stratified sampling to the income column.3) Train a Decision Tree model on the training set to predict whether or not a person earns more than 50K per year4) Apply the model to the test set5) Evaluate the accuracy of the model with scoring metrics- What is the overall accuracy of the model?86%6) Open the configuration dialog of the Scorer (JavaScript) node and exclude those statistics from the classprediction statistics table that are also present in the confusion matrix. Display the number of rows in the confusionmatrix. - What is the number of rows in the test dataset?81937) Evaluate the performance of the model with an ROC curve- What is the Area under the Curve statistics for the decision tree model?.888) OPTIONAL: Try out other parameter settings to reach a better performance. For example, change the qualitymeasure, pruning method, or minimum number of records. Node 13Node 14Node 15Read adult_joined.tablescore Partitioning DecisionTree Learner Decision TreePredictor Table Reader Scorer (JavaScript) ROC Curve

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Created by: Maarit Laukkanen
Created at: 2020-04-20
On NodePit since: 2023-08-28
Last update: 2026-01-06
Created with KNIME version: v4.3.2
Tags: classificationdecision treeoverall accuracyscorerROC curveAuC

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