Model Validation

Model validation involves assessing a model's performance on a separate validation dataset that was not used during training. This process helps to determine how well the model is likely to perform in real-world scenarios. Common techniques for model validation include k-fold cross-validation and holdout validation, which provide insights into the model's generalization capabilities.