![]() To plot a biplot, we will first define a biplot function to combine the component scores, loading vectors and variable names. They enable the user to grasp what the components represent and each variable’s share in these representations. Biplots are used in general for this purpose. In order to understand the relation between the principal components and the original variables, a visual that displays both elements are needed. Visualisation of Component-Variable Relation If you want to learn more about how to interpret a scree plot and how to implement it in Python, check our tutorials: Scree Plot for PCA Explained and Scree Plot in Python. ylabel ( 'Proportion of Variance Explained' )įigure 3 shows a scree plot for the first six principal components of our PCA. ![]() As the array starts from 0, we will add 1 to the equation to start the x-axis values from 1. To plot a scree plot, first, we will create an array containing the principal component numbers via np.arange(pca.n_components_). ![]() The scree plots are specialized for this kind of visualization in factor analyses. Visualizing the explained variance per principal component is useful for deciding on the ideal number of components to retain in the analysis. You can also check our tutorial Draw 3D Plot of PCA in Python to see another example of plotting a 3D scatterplot for a PCA. ![]()
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