What is cross-validation? Why is it used?

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Cross-validation is a technique used in machine learning to evaluate how well a model generalizes to unseen data. It involves splitting the dataset into multiple parts to train and test the model repeatedly, providing a more reliable estimate of its performance than a single train-test split.

How Cross-Validation Works:

The most common method is k-fold cross-validation:

• The data is divided into k equal-sized folds (subsets).

• The model trains on k-1 folds and tests on the remaining fold.

• This process repeats k times, each time with a different fold as the test set.

• The performance metrics (accuracy, RMSE, etc.) from all k runs are averaged to get a robust estimate.

Why Cross-Validation is Used:

1. Better Performance Estimation:
   It reduces the risk of overfitting or underfitting by testing the model on multiple subsets, offering a more accurate picture of how the model will perform on new data.

2. Efficient Use of Data:
   All data points are used for both training and testing, maximizing data usage, which is especially valuable for small datasets.

3. Model Comparison:
   Cross-validation allows fair comparison between different models or hyperparameters by evaluating each under the same conditions.

4. Detects Overfitting:
   If a model performs well on training data but poorly in cross-validation, it signals overfitting.

Summary:

Cross-validation improves model reliability by repeatedly testing on different data subsets, helping build models that generalize well to real-world data.

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