What is K-Fold Cross validation?
K-Fold Cross-validation is a technique used in machine learning to assess the performance and generalizability of a model. The basic idea is to partition the dataset into “K” subsets (folds) of approximately equal size. The model is trained K times, each time using K-1 folds for training and the remaining fold for validation. This process is repeated K times, with a different fold used as the validation set in each iteration.
K-Fold Cross-validation helps in obtaining a more reliable estimate of a model’s performance by reducing the impact of the specific data split on the evaluation. It is particularly useful when the dataset is limited or when there is a concern about the randomness of the data partitioning.
Common choices for K include 5, 10, or sometimes even higher values, depending on the size of the dataset and the computational resources available. In the extreme case where K equals the total number of samples in the dataset, it is called “Leave-One-Out Cross-validation” (LOOCV). However, LOOCV can be computationally expensive and might not be practical for large datasets.
The dataset is divided into k equal-sized partitions at random for k fold cross validation. For greater randomization, D may occasionally be shuffled before to cross validation. We usually have k = 2, 5, 10 (10 is most common). For D = 250 and K = 5, each fold will contains 50 data.
Steps for K-Fold Cross-validation are as follows:
- Shuffled data for randomization.
- Divide the dataset into K subsets or folds.
- Train-Validation Loop: For each iteration:
- Use K-1 folds for training the model.
- Use the remaining fold for validation.
- Evaluate the model’s performance on each validation set using a predefined metric (e.g., accuracy, precision, recall, F1 score).
- Calculate the average performance across all K iterations.
How K-Fold Prevents overfitting in a model?
In machine learning, accurately processing how well a model performs and whether it can handle new data is crucial. Yet, with limited data or concerns about generalization, traditional methods of evaluation may not cut it. That’s where cross-validation steps in. It’s a method that rigorously tests predictive models by splitting the data, training on one part, and testing on another. Among these methods, K-Fold Cross-validation shines as a reliable and popular choice.
In this article, we’ll look at the K-Fold cross-validation approach and how it helps to reduce overfitting in models.
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