What is the effect of standardizing continuous features before applying Principal Component Analysis?
Understand the Problem
The question is asking about the impact of standardizing continuous features prior to performing Principal Component Analysis (PCA). The focus is on understanding how standardization influences the results and the advantages it may provide, such as ensuring that features with larger scales do not disproportionately affect the analysis.
Answer
Standardizing ensures equal scaling of features, preventing bias in PCA due to varying feature scales.
Standardizing continuous features before applying PCA ensures that each feature contributes equally by giving them the same scale, allowing PCA to project data based purely on variance rather than being influenced by 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 project data based purely on variance rather than being influenced by varying feature scales.
More Information
PCA is sensitive to the scale because it focuses on variance maximization. If features have different scales, those with larger scales may dominate the principal components, skewing the analysis.
Tips
A common mistake is not standardizing the data before PCA, which can lead to misleading results due to unequal feature contributions.
Sources
- Importance of Feature Scaling — scikit-learn 1.5.2 documentation - scikit-learn.org
- Checks and Data Preprocessing Steps Before Applying PCA - safjan.com
- Why do we need to normalize data before principal component ... - stats.stackexchange.com
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