Itqualityassurance
- How much variance should PCA explain?
- What is a high loading in PCA?
- Should I remove highly correlated features before PCA?
- What do the loadings of a PCA tell us?
How much variance should PCA explain?
Some criteria say that the total variance explained by all components should be between 70% to 80% variance, which in this case would mean about four to five components.
What is a high loading in PCA?
The loadings are from a numerical point of view, equal to the coefficients of the variables, and provide information about which variables give the largest contribution to the components. Loadings range from -1 to 1. A high absolute value (towards 1 or -1) describes that the variable strongly influences the component.
Should I remove highly correlated features before PCA?
Hi Yong, PCA is a way to deal with highly correlated variables, so there is no need to remove them. If N variables are highly correlated than they will all load out on the SAME Principal Component (Eigenvector), not different ones. This is how you identify them as being highly correlated.
What do the loadings of a PCA tell us?
Positive loadings indicate a variable and a principal component are positively correlated: an increase in one results in an increase in the other. Negative loadings indicate a negative correlation. Large (either positive or negative) loadings indicate that a variable has a strong effect on that principal component.
