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DC-DR-01

Apply Principal Component Analysis - Transform the original feature space into a set of principal components- First m components preserve the maximum of variability (information) from the original feature space- Preserve 80% of information Decision Tree 10FCVSonar Data SetInitial DataFiltered DataDecision Tree 10FCVConfusion MatrixConfusion MatrixCompute PCAtransformationProject original featuresto the principal componentsKeep 10 firstprincipal components Cross Validation ARFF Reader InteractiveTable (local) InteractiveTable (local) Cross Validation Scorer Scorer PCA Compute PCA Apply Apply Principal Component Analysis - Transform the original feature space into a set of principal components- First m components preserve the maximum of variability (information) from the original feature space- Preserve 80% of information Decision Tree 10FCVSonar Data SetInitial DataFiltered DataDecision Tree 10FCVConfusion MatrixConfusion MatrixCompute PCAtransformationProject original featuresto the principal componentsKeep 10 firstprincipal components Cross Validation ARFF Reader InteractiveTable (local) InteractiveTable (local) Cross Validation Scorer Scorer PCA Compute PCA Apply

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Created by: salvadorgarcialopez
Created at: 2022-04-05
On NodePit since: 2025-04-30
Last update: 2025-05-11
Created with KNIME version: v4.5.1

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