What is cross-validation?

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Cross-validation is a statistical technique used in machine learning to evaluate how well a model generalizes to unseen data. Instead of training and testing a model on the same dataset, cross-validation splits the data into multiple parts to ensure the model's performance is reliable and not just fitted to one specific dataset.

The most common form is k-fold cross-validation, where:

1. The dataset is divided into k equally sized folds (e.g., 5 or 10).

2. The model is trained on k-1 folds and tested on the remaining fold.

3. This process is repeated k times, each time using a different fold for testing.

4. The final performance metric is the average of all k test results.

This helps in:

• Reducing overfitting, since the model is validated on different subsets.

• Providing a more accurate estimate of model performance on unseen data.

There are other variations too:

• Stratified k-fold: Ensures each fold has a similar class distribution (used in classification problems).

• Leave-one-out (LOOCV): A special case where k equals the number of data points—computationally expensive but thorough.

• Time series cross-validation: Respects temporal order for time-dependent data.

Cross-validation is crucial during model selection and hyperparameter tuning, helping you choose models that are not just accurate but also robust and generalizable.

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