What is Overfitting?
Overfitting happens when a machine learning model learns the training data so well that it detects noise or random oscillations in the data as meaningful patterns. This can result in poor performance when the model is applied to new, previously unseen data since it does not generalize properly.
Overfitting can be reduced by using:
- Regularization
- Cross validation
- Early stopping
- Dropout
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.