Machine Learning: Introduction to Cross Validation

This article introduces cross-validation, a pivotal model validation technique. It assesses a model’s performance on an independent dataset, mitigating overfitting by iteratively training and testing subsets. It aids hyperparameter optimization, ensuring robustness for new data.

src: https://www.analyticsvidhya.com/blog/2022/02/k-fold-cross-validation-technique-and-its-essentials/

Cross-validation is a model validation technique used to evaluate the performance of a model on an independent dataset. It splits the dataset into multiple subsets and iteratively trains the model on a subset and tests it on the remaining subset.

The basic idea behind cross-validation is to simulate the process of training a model on a dataset and then testing it on new, unseen data. By doing this, cross-validation helps to reduce the risk of overfitting, which occurs when the model performs well on the training data but poorly on new data.

There are several types of cross-validation techniques, but the most commonly used ones are:

1. k-Fold Cross-Validation: This technique involves splitting the dataset into k equal-sized subsets, called folds. The model is trained on k-1 folds and tested on the remaining fold. This process is…