You're working with a dataset where different features are on wildly different scales. How can dimensionality reduction techniques like PCA be adapted to this scenario?
- Apply PCA without any preprocessing
- Ignore the scales
- Scale the features before applying PCA
- Use only large-scale features
When features are on different scales, scaling them before applying PCA is crucial. Standardizing the features ensures that each one contributes equally to the calculation of the principal components, which is vital for the accuracy of the transformation. Ignoring the scales, applying PCA without preprocessing, or focusing only on large-scale features may lead to biased or incorrect results.
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