The higher the absolute value, the higher the influence of the input dimension on the principal component.

The first column in the table contains the component's eigenvalue, a high value indicates a high variance (or in other words, the respective component dominates the orientation of the input data).Įach subsequent column (labeled with the name of the selected input column) contains a coefficient representing the influence of the respective input dimension to the principal component. variance along the corresponding principal axis. Each row in the table represents one principal component, whereby the rows are sorted with decreasing eigenvalues, i.e. Table containing parameters extracted from the PCA.