Icon

Group 07_​Day1

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
Instructions on how to use the nodes in the four "[Model name] validation and important metrics" boxes. 1. These boxes contain all the necessary nodes to evalute the performance of a model. Hence, you still need to do all the work of preparation, training, and so on. 2. You need to make sure the "[Model Name] Predictor" nodes are set up as follows (so that the metanode that computes the F2 score can read them): Gradient Boosted Trees Predictor: - Tick all boxes in the Prediction Settings menu.- The prediction column name is "Prediction (GB)".- The suffix for proability columns is "GB". Decision Tree Predictor:- Maximum number of stored patterns is 20,000. - Tick all boxes in the Options menu. - The prediction column name is "Prediction (DT)"- The suffix for probability columns is "DT". Random Forest Predictor:- Tick all boxes in the Prediction settings menu except "Use soft voting".- The prediction column name is "Prediction (RF)".- The suffix for proability columns is "RF". Logistic Regression Predictor: - Tick all boxes in the Settings menu.- The prediction column name is "Prediction (LR)".- The suffix for probability columns is "LR".
If you encounter a problem with the meta node for F2 score, open it and follow the instructions. Do not take for granted that the Lift chart, ROC curve, and Scorer nodes are properly set.
If you encounter a problem with the meta node for F2 score, open it and follow the instructions. Do not take for granted that the Binary Classification Inspector and Line Plot (JavaScript) nodes are properly configured.
Upload the score data set to predict the missing target variable with the champion model.
Feature preparation
Do not train the model again: use the trained champion model to predict new data.
Make sure to prepare the data in the exact same way as the other partitions. Do not sample and partition this data set. Do not train the model again: use the trained champion model to predict new data.
Make sure that the Binary Classification Inspector node only includes: - "Gradient boosting" - "Random forest" - "Decision tree" - "Logistic regression"
Selecting best F2 cut-off
Top k Row Filter
Lift Chart (JavaScript) (legacy)
ROC Curve (JavaScript) (legacy)
Generating F2
Precision & Recall
Data Explorer
Adjust prediction based on cutoff value of your champion AI model
Column Expressions (legacy)
Training
Logistic Regression Learner
Column Filter
Training
Random Forest Learner
75/25
Table Partitioner
Training
Decision Tree Learner
60/40
Table Partitioner
Shuffle
Training
Gradient Boosted Trees Learner
Fraud
Equal Size Sampling
Annual Income
Histogram
Age of Driver
Histogram
Vehicle Price
Histogram
Confusion matrix and ROC
Binary Classification Inspector
Annual Income
Box Plot
Age of Driver
Box Plot
Scorer (JavaScript)
Treat outliers (<>1.5 IQR) as missing
Numeric Outliers
Scorer (JavaScript)
Vehicle Price
Box Plot
Scorer (JavaScript)
select top 3 models based on f2 score
Top k Row Filter
Scorer (JavaScript)
Treat outliers (<>1.5 IQR) as missing
Numeric Outliers
Missing Value
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
Missing Value
lift chart
Line Plot (JavaScript) (legacy)
Generating F2
Precision & Recall
Lift Chart (JavaScript) (legacy)
Treat outliers (<>1.5 IQR) as missing
Numeric Outliers (Apply)
Selecting best F2 cut-off
Top k Row Filter
Missing Value
ROC Curve (JavaScript) (legacy)
Normalizer
Normalizer
Gradient Boosted Trees Predictor
Generating F2
Precision & Recall
Gradient Boosted Trees Predictor
Missing Value
Column Filter
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)

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: 2026-04-08
Last update: 2026-04-08
Created with KNIME version: v5.11.0

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

Try NodePit Runner!