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Knime_​first_​Assignment (1)

Load and Filter Data

Feature Selection

Model Training and Testing

Data fidelity preservation steps taken to handle errors between XGBoost and PCA. Used for training and test sets.

Error Handling
PCA Apply
Number to String
Accuracy:90.5% approx
Scorer
Top: train (70%)Bottom: test (30%)
Table Partitioner
PCA
PCA Compute
Inspect ROC Curve
ROC Curve
Column Filter
XGBoost Tree Ensemble Learner
Normalizer (Apply)
XGBoost Predictor
Feature Selection Filter
Duplicate Row Filter
Maintains target column to be added to the filtered data for testing.
Column Filter
SMOTE
Removes target column to maintain data fidelity issue.
Column Filter
Column Appender
Local File System Connector
Column Filter
Random Forest Learner
Recombines the target column with the main filtered table.
Column Appender
Feature Selection Loop Start (1:1)
PCA Apply
Loan approvaldataset 1 = approved; 0 = rejected
CSV Reader
Feature Selection Loop End
Normalizer
Random Forest Predictor
Scorer
Feature Selection Filter

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: HAhadyDolatsara
Created at: 2025-03-20
On NodePit since: 2026-03-29
Last update: 2026-04-18
Created with KNIME version: v5.11.0

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