It is used to visualize the importance of each principal component and can be used to determine the number of principal components to retain

he result of the correlation matrix can be interpreted as follow: The higher the value, the most positively correlated the two variables are. The closer the value to -1, the most negatively correlated they are.

With the biplot, it is possible to visualize the similarities and dissimilarities between the samples, and further shows the impact of each attribute on each of the principal components.

how much each variable is represented in a given component. Such a quality of representation is called the Cos2 and corresponds to the square cosine. A low value means that the variable is not perfectly represented by that component. A high value, on the other hand, means a good representation of the variable on that component.

biplot and attributes importance can be combined to create a single biplot, where attributes with similar cos2 scores will have similar colors.