Principal Component Analysis - IT Assignment Help

Assignment Task

Task

Each principal component has associated to it the fraction of total variance it “explains”. The components explaining the most variance are generally the most informative. Usually, the first two PCs are used for plotting. We’re also “throwing away” data, but this data is usually less relevant because it “explains” less variance. 

PCA: Scores and Loadings

• So far, we only looked at the Scores of points in the Iris data set.
• The score of a data point is its position on the principal component.
• The loadings of a PCA describe what a principal component encodes.
•  Loadings for PC 1 in the iris data set:

Use cases for PCA

• Dimensionality reduction for visualisation
• Estimation of the number of relevant dimensions via explained variance.
• Interpretation of variable importance via the loadings of each variable on the principal components

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