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Group04_​Day5_​Final

Exploration
Pre-processing
Data partitioning and sampling
Feature preparation
Random forest training
Gradient boosting validation and important metrics
Decision tree training
Logistic regression training
Gradient boosting training
Random forest validation and important metrics
Decision tree validation and important metrics
Logistic regression validation and important metrics
Comparing the performance metrics of the models
Champion model testing and important metrics

Feature preparation

Selecting best F2 cut-off
Top k Row Filter
Lift Chart (JavaScript) (legacy)
ROC Curve (JavaScript) (legacy)
Generating F2
Precision & Recall
Column Filter
Adjust prediction based on cutoff value of your champion AI model
Column Expressions (legacy)
Training
Logistic Regression Learner
Statistics View
Statistics
Training
Random Forest Learner
Missing Value
Training
Decision Tree Learner
Data Explorer
Numeric Outliers
Training
Gradient Boosted Trees Learner
Before
Box Plot
After Winzorization
Box Plot
Histogram
Histogram
Confusion matrix and ROC
Binary Classification Inspector
Linear Correlation
Histogram
Scorer (JavaScript)
90/10
Table Partitioner
Scorer (JavaScript)
Value Counter
Scorer (JavaScript)
Equal Size Sampling
select top 3 models based on f2 score
Top k Row Filter
Scorer (JavaScript)
67/33
Table Partitioner
Value Counter
auto_claims.csv(Data set for training, validation,and testing)
CSV Reader
auto_claims_score.csv (Data set for scoring)
CSV Reader
Create an Excel file with the model's outputs
Excel Writer
Selecting best F2 cut-off
Top k Row Filter
Lift Chart (JavaScript) (legacy)
Validation
Logistic Regression Predictor
ROC Curve (JavaScript) (legacy)
Lift & Gain table
RowID
Validation
Gradient Boosted Trees Predictor
Validation
Random Forest Predictor
One to Many
lift chart
Line Plot (JavaScript) (legacy)
Generating F2
Precision & Recall
Lift Chart (JavaScript) (legacy)
Validation
Gradient Boosted Trees Predictor
Selecting best F2 cut-off
Top k Row Filter
One to Many
ROC Curve (JavaScript) (legacy)
Missing Value
Column Filter
One to Many
Generating F2
Precision & Recall
Numeric Outliers
Before
Box Plot
Gradient Boosted Trees Predictor
Before
Box Plot
Joins 2 models
Joiner
Extract Header & Transpose
Sert Color
Binary Classification Inspector
Replace P (fraud =1) with model name
Column Renamer
Generating F2
Precision & Recall
Joins 2 models
Joiner
Selecting best F2 cut-off
Top k Row Filter
Joins 4 models
Joiner
Lift Meta node
Precision & Recall
Lift Chart (JavaScript) (legacy)
Validation
Decision Tree Predictor
ROC Curve (JavaScript) (legacy)
Before
Box Plot

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: antoi
Created at: 2023-11-02
On NodePit since: 2026-04-21
Last update: 2026-04-21
Created with KNIME version: v5.11.0

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