A machine learning model is overfitting on a training dataset. How could feature selection be used to address this issue?
- By increasing the model complexity
- By increasing the number of features
- By reducing the number of features
- By transforming the features
Feature selection can be used to address overfitting by reducing the number of features. Overfitting occurs when a model learns the noise in the training data, leading to poor performance on unseen data. By reducing the number of features, the complexity of the model can be reduced, which in turn can help to mitigate overfitting.
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