What is Cross validation?
A method for evaluating a predictive model’s effectiveness and capacity for generalization is called cross-validation. The dataset is divided into subsets, the model is fitted to one of the subsets (the training set), and the model is assessed on the complementary subset (the validation set). The performance numbers are averaged over the course of several rounds of this operation, each with a distinct split.
There are various approaches to cross-validation; K-Fold Cross-validation is one of the more well-known techniques.
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.
Contact Us