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07_​Dimensionality_​Reduction

Dimensionality Reduction
Dimensionality Reduction by Low Variance Dimensionality Reduction by Linear Correlation Exercise: Dimensionality Reduction1) Filter out columns in the training set that have- Variance lower than 0.2 (Low Variance Filter node)- Linear Correlation higher or equal to 0.8 with another column (Linear Correlation and Correlation Filter nodes)2) Apply automatic dimensionality reduction by replacing the numeric columns with principal components. Retain 90 % of the information in the original numeric columns.(PCA Compute and PCA Apply nodes)3) Apply these dimensionality reduction techniques to the test set (Reference Column Filter and PCA Apply nodes) Dimensionality Reduction by PCA Read AmesHousing.csv Preprocessing CSV Reader Dimensionality Reduction by Low Variance Dimensionality Reduction by Linear Correlation Exercise: Dimensionality Reduction1) Filter out columns in the training set that have- Variance lower than 0.2 (Low Variance Filter node)- Linear Correlation higher or equal to 0.8 with another column (Linear Correlation and Correlation Filter nodes)2) Apply automatic dimensionality reduction by replacing the numeric columns with principal components. Retain 90 % of the information in the original numeric columns.(PCA Compute and PCA Apply nodes)3) Apply these dimensionality reduction techniques to the test set (Reference Column Filter and PCA Apply nodes) Dimensionality Reduction by PCA Read AmesHousing.csv Preprocessing CSV Reader

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Created by: Emilio Silvestri
Created at: 2019-11-29
On NodePit since: 2021-07-13
Last update: 2024-11-04
Created with KNIME version: v4.4.0
Tags: dimensionality reductiondata manipulationpreprocessingPCAfeature importanceeducation

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