What is the significance of dividing a dataset into training and testing sets, and how does it affect model evaluation?
- Enhances prediction; Reduces accuracy
- Enhances training; Reduces testing
- Helps in learning; Assesses generalization
- Improves clustering; Affects regression
Dividing a dataset into training and testing sets helps the model to learn patterns from the training set and assesses its generalization to unseen data using the testing set. It ensures that the model's performance is evaluated on data not used during training.
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