What are the Common Techniques for Model Validation?
1. Train-Test Split This technique involves splitting the data into two parts: a training set and a testing set. The model is trained on the training set and then tested on the testing set. This helps in evaluating how well the model generalizes to new, unseen data.
2. Cross-Validation Cross-validation is a more robust technique where the data is divided into multiple subsets. The model is trained and tested multiple times, each time using a different subset as the testing set and the remaining subsets as the training set. This provides a more comprehensive evaluation of the model's performance.
3. Bootstrapping Bootstrapping involves generating multiple random samples from the original data (with replacement) and then training and testing the model on these samples. This technique helps in estimating the variability of the model's performance.