Icon

01_​Analyze_​Training_​a_​Churn_​Predictor

Analyze Data by Training a Decision Tree Classifier for Churn Prediction

This workflow is an example of how to train a basic machine learning model for a churn prediction task. In this case, we use a Decision Tree algorithm. However, the Learner-Predictor construct is common to all supervised algorithms.








Pre-processing / Data Preparation Read Data from differentfiles - Contract Data - Calls Data Color Churn 0 -> Blue 1 -> Red Conversions - Convert the column "Churn"and "Area Code" to String Partitioning 80% training 20% testing Train Model Decision tree Save to PMML Evaluation Apply and score Model How to train Classification Model? Step 1: Drag the "Decision Tree Learner" node anddouble click to open the dialog Step 2: Select the "Class column" as "Churn" and"Quality measure" as "Gini Index"Step 3: RIght Click on the node and select "Executeand Open View" to train the model and to get a view ofthe Decision Tree How to evaluate Classification Model? Step 1: Drag the Decision Tree Predictor node. Step 2: Connect the output of "Decision TreeLearner" node to Port 0 and Test Dataset to Port 1.Execute the nodeStep 3: Connect the Predictor Output to "ROCCurve" and "Scorer" node to evaluate the model onvarious evaluation measures 80%vs. 20%Port 0: Train setPort 1: Test setcolorby churnclass = churnarea codeand churn ->StringAuCCalls datacontract dataperformancescoringapply decision tree Partitioning Color Manager DecisionTree Learner Number To String ROC Curve Excel Reader PMML Writer CSV Reader Scorer Decision TreePredictor Joiner Pre-processing / Data Preparation Read Data from differentfiles - Contract Data - Calls Data Color Churn 0 -> Blue 1 -> Red Conversions - Convert the column "Churn"and "Area Code" to String Partitioning 80% training 20% testing Train Model Decision tree Save to PMML Evaluation Apply and score Model How to train Classification Model? Step 1: Drag the "Decision Tree Learner" node anddouble click to open the dialog Step 2: Select the "Class column" as "Churn" and"Quality measure" as "Gini Index"Step 3: RIght Click on the node and select "Executeand Open View" to train the model and to get a view ofthe Decision Tree How to evaluate Classification Model? Step 1: Drag the Decision Tree Predictor node. Step 2: Connect the output of "Decision TreeLearner" node to Port 0 and Test Dataset to Port 1.Execute the nodeStep 3: Connect the Predictor Output to "ROCCurve" and "Scorer" node to evaluate the model onvarious evaluation measures 80%vs. 20%Port 0: Train setPort 1: Test setcolorby churnclass = churnarea codeand churn ->StringAuCCalls datacontract dataperformancescoringapply decision tree Partitioning Color Manager DecisionTree Learner Number To String ROC Curve Excel Reader PMML Writer CSV Reader Scorer Decision TreePredictor Joiner

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: rosy
Created at: 2015-04-23
On NodePit since: 2023-02-26
Last update: 2026-01-25
Created with KNIME version: v4.5.2
Tags: Customer IntelligenceCIchurndecision treePracticing Data ScienceBeginnerBeginner Cheat SheetBeginners Space

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!