• How it works: The simplest data splitting strategy. You divide your dataset into two subsets:
  • Training set: Used to train the model.
  • Test set: Held out for final evaluation on unseen data.
• Strengths:
  • Very fast and easy to implement.
• Weaknesses:
  • Performance highly depends on how the split is done (risk of bias).
  • Provides only a single measurement of performance, which might not generalize well.
• Use Cases:
  • Quick exploration when you have a large dataset and cross-validation might be too computationally expensive.
  • Acceptable for the final evaluation after using cross-validation to choose your model and tune hyperparameters.

Train-Test Split vs. Cross-Validation

The train-test split offers a quick glimpse of performance, but cross-validation is often favored for robust model evaluation. This is because:

• Reduced Bias: A single train-test split might not represent the entire dataset well. Cross-validation uses multiple splits, offering a more reliable model performance assessment.
• Hyperparameter Tuning: Cross-validation helps select the best hyperparameters by trying them across different sets of data. A simple train-test split lacks this ability.
• Overfitting Detection: Cross-validation is less prone to overfitting by averaging results over multiple folds.

When to Use Each:

1. Initial Exploration: A simple train-test split can be a starting point for initial model prototyping.
2. Model Selection: Cross-validation is typically the preferred method during model selection and hyperparameter tuning.
3. Final Evaluation: After cross-validation, a model should always be evaluated on a completely held-out test set not involved in any other part of the development process.