K-fold cross-validation

K-fold cross-validation is not an algorithm itself; it is a technique or method used for model evaluation. It helps assess how well a machine learning model performs by splitting the dataset into multiple parts (folds) and repeatedly training and testing the model on different subsets of the data. 

For instance, suppose you want to test three algorithms—Algorithm A, Algorithm B, and Algorithm C using a dataset with 10 data points and 5-fold cross-validation. You start by dividing the dataset into 5 folds, each containing 2 data points: Fold 1 (data points 1 and 2), Fold 2 (data points 3 and 4), Fold 3 (data points 5 and 6), Fold 4 (data points 7 and 8), and Fold 5 (data points 9 and 10). For each algorithm, you train the model on 4 of the 5 folds and test it on the remaining fold. You repeat this process for all 5 folds, recording the performance score each time and then averaging these scores.

For example, after applying this method:

  • Algorithm A might have an average performance score of 80%.
  • Algorithm B could achieve an average performance score of 85%.
  • Algorithm C might end up with a lower average score of 75%.

Comparing these average performance scores, Algorithm B shows the highest average score of 85%, indicating it works best for your dataset. Therefore, based on K-fold cross-validation, Algorithm B is the most effective algorithm among the three tested.


Imagine you have a dataset with 100 samples, and there are two classes: Class A (90 samples) and Class B (10 samples).
  • K-Fold Cross-Validation: If you use K-Fold with 5 folds, you might end up with some folds having very few samples from Class B or none at all, which could lead to unreliable performance metrics.
  • Stratified K-Fold Cross-Validation: Ensures that each fold has approximately 18 samples from Class A and 2 samples from Class B (in the case of 5 folds), maintaining the class distribution and providing a more balanced evaluation.

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