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 repeated k times, with each fold being used as the testing set once.
2. Leave-One-Out Cross-Validation: This technique involves training the model on all but one data point and testing it on the left-out data point. This process is repeated for each data point in the dataset.
3. Stratified Cross-Validation: This technique is used when the dataset is imbalanced, meaning that there are significantly more samples of one class than the other. In this technique, the dataset is split into k folds, and each fold contains roughly the same proportion of samples from each class.

Cross-validation is used to evaluate the performance of a model by providing an estimate of how well the model will perform on new, unseen data.

It can also be used to tune the hyperparameters of a model, which are the settings that control the behavior of the model during training. By comparing the performance of a model with different hyperparameter settings using cross-validation, we can choose the best set of hyperparameters that optimize the model’s performance on new data.