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justknimeit-23part2_​Victor

Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, going tocancel their contracts) based on attributes of their accounts. One of your colleagues said that she was able to achieve a bit over 95%accuracy for the test data without modifying the training data at all, and using all given attributes exactly as they are. Again, the targetclass to be predicted is Churn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). Whatmodel should you train over the training dataset to obtain this accuracy over the test dataset? Can this decision be automated? Note 1: A simple, automated solution to this challenge consists of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use all availableattributes. Note 3: Need more help to understand the problem? Check this blog post out. XGBoost Performance metricssummaryAccuracy : 95,502%Error : 4,498%Cohen's Kappa : 0,814 Training dataTest dataViewXGBoost treeperformances andconfusion matrixXGBoost trainingXGBoost predictions CSV Reader CSV Reader Scorer XGBoost TreeEnsemble Learner XGBoost Predictor Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, going tocancel their contracts) based on attributes of their accounts. One of your colleagues said that she was able to achieve a bit over 95%accuracy for the test data without modifying the training data at all, and using all given attributes exactly as they are. Again, the targetclass to be predicted is Churn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). Whatmodel should you train over the training dataset to obtain this accuracy over the test dataset? Can this decision be automated? Note 1: A simple, automated solution to this challenge consists of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class in the datasets, and use all availableattributes. Note 3: Need more help to understand the problem? Check this blog post out. XGBoost Performance metricssummaryAccuracy : 95,502%Error : 4,498%Cohen's Kappa : 0,814 Training dataTest dataViewXGBoost treeperformances andconfusion matrixXGBoost trainingXGBoost predictionsCSV Reader CSV Reader Scorer XGBoost TreeEnsemble Learner XGBoost Predictor

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Created by: victo
Created at: 2022-07-03
On NodePit since: 2022-07-08
Last update: 2024-12-23
Created with KNIME version: v4.6.0

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