Win-Vector LLC’s Dr. Nina Zumel has a three part series on Principal Components Regression that we think is well worth your time.
- Part 1: the proper preparation of data (including scaling) and use of principal components analysis (particularly for supervised learning or regression).
- Part 2: the introduction of y-aware scaling to direct the principal components analysis to preserve variation correlated with the outcome we are trying to predict.
- Part 3: how to pick the number of components to retain for analysis.
Continue reading Why you should read Nina Zumel’s 3 part series on principal components analysis and regression
In our previous note we demonstrated Y-Aware PCA and other y-aware approaches to dimensionality reduction in a predictive modeling context, specifically Principal Components Regression (PCR). For our examples, we selected the appropriate number of principal components by eye. In this note, we will look at ways to select the appropriate number of principal components in a more automated fashion.
Continue reading Principal Components Regression, Pt. 3: Picking the Number of Components