Dimensionality reduction is often used to overcome the ___________ problem, where having too many features relative to the number of observations can lead to overfitting.
- curse of dimensionality
- multicollinearity
- overfitting
- scaling
The overfitting problem occurs when a model is too complex relative to the amount and noise of the data, which can happen when there are too many features. Dimensionality reduction techniques can help by simplifying the feature space, reducing the risk of overfitting.
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