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Challange 24

Challange 24
KNIME IT Challange 24Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, goingto cancel 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 target class tobe predicted is Churn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). What model should youtrain over the training dataset to obtain this accuracy over the test dataset? Can this decision be automated? Note 1: A simple, automatedsolution to this challenge consists of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class inthe datasets, and use all available attributes. Note 3: Need more help to understand the problem? Check this blog post out. Node 1Node 2Node 5Node 6Node 7Node 9Node 10 CSV Reader CSV Reader Scorer Parameter OptimizationLoop Start ParameterOptimization Loop End Gradient BoostedTrees Learner Gradient BoostedTrees Predictor KNIME IT Challange 24Description: Just like in last week’s challenge, a telecom company wants you to predict which customers are going to churn (that is, goingto cancel 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 target class tobe predicted is Churn (value 0 corresponds to customers that do not churn, and 1 corresponds to those who do). What model should youtrain over the training dataset to obtain this accuracy over the test dataset? Can this decision be automated? Note 1: A simple, automatedsolution to this challenge consists of 5 nodes. Note 2: In this challenge, do not change the statistical distribution of any attribute or class inthe datasets, and use all available attributes. Note 3: Need more help to understand the problem? Check this blog post out. Node 1Node 2Node 5Node 6Node 7Node 9Node 10 CSV Reader CSV Reader Scorer Parameter OptimizationLoop Start ParameterOptimization Loop End Gradient BoostedTrees Learner Gradient BoostedTrees Predictor

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Created by: ruben_gelder
Created at: 2022-06-29
On NodePit since: 2022-07-09
Last update: 2024-04-28
Created with KNIME version: v4.5.2
Tags: Transfer-Solutionsjustknimeit-24

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