Holdout Method

The Holdout Method is a way to evaluate how well a model predicts new data. It involves splitting your dataset into two parts: one for training the model and another independent part for testing its performance. After training the model on the first set, you assess how accurately it makes predictions on the holdout set. This helps determine if the model is likely to perform well on unseen data, minimizing overfitting. It's a straightforward approach to estimate the model's generalization ability before applying it to real-world scenarios.