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Zielgruppenanalyse(noted)

Decision Tree

After the data is partitioned into train and test set, a decision tree model is trained and applied.

Name: IAS-EntwicklungAPI-Key: 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
BikeBuyer:non-BikeBuyer ratio = 62:38
70% for training set30% for test set
Table Partitioner
2x-10x Cross Validations
String Manipulation
MinRecords Param Optimization
Decision Tree Learner
Decision Tree Predictor
Num of validations = 10
X-Partitioner
X-Aggregator
Lift Chart (JavaScript) (legacy)
accu 79,131%, cohen 0,553%
Scorer
Prediction (BikeBuyer)=1
Row Filter
Target Group Ana
LLM Prompter
GroupBy
Rule Engine
Group by BikeBuyer
GroupBy
Table View (JavaScript) (legacy)
ROC Curve
Credentials Configuration
read vTargetMail.table
Table Reader
Decision Tree Predictor
OpenAI Authenticator
OpenAI LLM Selector
<=65 + Australia
Rule-based Row Filter
Lift Chart (JavaScript) (legacy)
filter columns
Table Manipulator
MinRecords = 3
Decision Tree Learner
BikeBuyer to String
Number to String (PMML)
accu 78,056% cohen 0,512%
Scorer

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On NodePit since: 2026-01-21
Last update: 2026-01-22
Created with KNIME version: v5.8.0

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