Practical Predictive Analytics

Principal Components Analysis (PCA)

Principle Components Analysis (PCA) is a variable reduction technique, and can also be used to identify variable importance. An interesting benefit of PCA is that all of the resulting new component variables will all be uncorrelated with each other. Uncorrelated variables are desirable in a predictive model since too many correlated variables confound predictions and make it difficult to tell which of the independent variables have the most influence. So, if you first perform an exploratory analysis of your data and you find that a high number of correlations exist, this would be a good opportunity to apply PCA.

Models can tolerate some degree of correlated variables. The situations I am speaking of are ...