What is the effect of standardizing continuous features before applying Principal Component Analysis?
Understand the Problem
The question is asking for the effect of standardizing continuous features prior to applying Principal Component Analysis (PCA). It presents multiple-choice options that suggest various impacts standardization may have on the analysis process. The focus is on understanding how standardization influences the behavior and results of PCA, particularly in terms of scale and interpretability.
Answer
Standardizing ensures each feature contributes equally by giving them the same scale for PCA.
Standardizing continuous features before applying PCA ensures that each feature contributes equally by giving them the same scale, allowing PCA to focus purely on variance rather than varying feature scales.
Answer for screen readers
Standardizing continuous features before applying PCA ensures that each feature contributes equally by giving them the same scale, allowing PCA to focus purely on variance rather than varying feature scales.
More Information
Standardization before PCA is vital because PCA attempts to find the directions (principal components) that maximize variance. Without standardization, features with larger scales can disproportionately influence these components.
Tips
A common mistake is to perform PCA on unstandardized data when the features are on different scales, leading to biased results.
Sources
- Why do we need to normalize data before principal component ... - stats.stackexchange.com
- Principal Component Analysis (PCA) Explained | Built In - builtin.com
- Importance of Feature Scaling — scikit-learn 1.5.2 documentation - scikit-learn.org
