The Python code given above results in the following plot.. Data visualization is the most common application of PCA. Second component explains 7.3% variance. ; Supplementary individuals (in dark blue, rows 24:27) : The coordinates of these individuals will be predicted using the PCA information and parameters obtained with active individuals/variables ; Active variables (in pink, columns 1:10) : Variables that PCA will project the data onto a smaller subspace of k dimensions (where k < p) while retaining as much of the variation as possible.These k dimensions are known as the principal components.. By applying PCA, we lose some of the variance (i.e., 23, Sep 21. Principal Component Analysis (PCA) is a linear dimensionality reduction technique (algorithm) that transform a set of correlated variables (p) into a smaller k (k
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