Scale

Preserve scale during PCA

Preserve scale during PCA
  1. Should you scale before PCA?
  2. Why is scaling necessary for PCA?

Should you scale before PCA?

PCA is affected by scale, so you need to scale the features in your data before applying PCA. Use StandardScaler from Scikit Learn to standardize the dataset features onto unit scale (mean = 0 and standard deviation = 1) which is a requirement for the optimal performance of many Machine Learning algorithms.

Why is scaling necessary for PCA?

When dealing with data that has features with different scales, it's often important to scale the data first. This is because data that has larger values may sway the data even with relatively little variability. The combine data frame is loaded for you.

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