What potential problem might arise if you include a vast number of irrelevant features in your machine learning model?
- Increased accuracy
- Model convergence
- Overfitting
- Underfitting
Including a vast number of irrelevant features can lead to overfitting. Overfitting occurs when the model fits the noise in the data, resulting in poor generalization to new data. It's essential to select relevant features to improve model performance.
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