How does high dimensionality in the feature space increase susceptibility to overfitting?
Answer
When dimensions far exceed observations, the model has vast mathematical freedom to find spurious correlations that align perfectly with the small sample.
Having significantly more input features than data points grants the model too much space to maneuver, allowing it to create complex rules based on coincidental relationships within the limited sample.
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