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Student Decision Tree

Exercise 4: Decision Tree and Multiple Linear Regression Description: The dataset involves students' performance in secondary education of two Portuguese schools in the Mathematics (mat) subject. The prediction task aims to predict thelikelihood of the student passing or failing based on the factors of (1) first-period grade (G1), (2) second-period grade (G2), (3) workday alcohol consumption (Dalc), (4)weekend alcohol consumption (Walc), (5) weekly study time (studytime), and (6) home to school travel time (traveltime). The model to be evaluated in this prediction task is theDecision Tree Model. Steps: 1) Read student-mat.csv2) Delete rows that has missing values3) Add a pass or fail status column based on a set of rules4) Partition the data into a 80-20 training and test set ratio5) Train a decision tree model on the training set to predict the correlation of the selected variables to the pass or fail status6) Apply the model to the test set7) Evaluate the performance of the Decision Tree with the Scorer (JavaScript) and ROC Curve node. Read Students RecordsTop: Training Set (80%)Bottom: Test Set (20%)Take from TopTrain the Model to Predict the StatusApply the Model to the Test SetScoring MetricCreate Rule for Class Column CriteriaFilter Out 0 G3 Rows CSV Reader Partitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript) Rule Engine ROC Curve Row Filter Exercise 4: Decision Tree and Multiple Linear Regression Description: The dataset involves students' performance in secondary education of two Portuguese schools in the Mathematics (mat) subject. The prediction task aims to predict thelikelihood of the student passing or failing based on the factors of (1) first-period grade (G1), (2) second-period grade (G2), (3) workday alcohol consumption (Dalc), (4)weekend alcohol consumption (Walc), (5) weekly study time (studytime), and (6) home to school travel time (traveltime). The model to be evaluated in this prediction task is theDecision Tree Model. Steps: 1) Read student-mat.csv2) Delete rows that has missing values3) Add a pass or fail status column based on a set of rules4) Partition the data into a 80-20 training and test set ratio5) Train a decision tree model on the training set to predict the correlation of the selected variables to the pass or fail status6) Apply the model to the test set7) Evaluate the performance of the Decision Tree with the Scorer (JavaScript) and ROC Curve node. Read Students RecordsTop: Training Set (80%)Bottom: Test Set (20%)Take from TopTrain the Model to Predict the StatusApply the Model to the Test SetScoring MetricCreate Rule for Class Column CriteriaFilter Out 0 G3 Rows CSV Reader Partitioning DecisionTree Learner Decision TreePredictor Scorer (JavaScript) Rule Engine ROC Curve Row Filter

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Created by: Y2
Created at: 2021-08-16
On NodePit since: 2021-08-16
Last update: 2024-04-28
Created with KNIME version: v4.4.0

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