PCA for Dimensionality Reduction

Medium

machine-learning Google Palantir Bloomberg

Multiple Choice

The remaining 185 components are noise and should always be discarded You reduce the feature space from 200 to 15 while retaining most information, but the components lose direct interpretability The model accuracy will always improve because fewer features prevent overfitting You must keep all 200 components to avoid losing any predictive signal

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