Step 1: Understand what overfitting means. Overfitting happens when a model learns the training data too well, including its noise and outliers, making it perform poorly on new, unseen data.
Step 2: Learn about cross-validation. Cross-validation is a technique used to evaluate how well a model will perform on unseen data.
Step 3: Implement cross-validation. This involves splitting your dataset into multiple parts. You train the model on some parts and test it on others.
Step 4: Assess model performance. By using cross-validation, you can see how well your model performs on different subsets of data, which helps you understand its generalization ability.
Step 5: Reduce overfitting risk. Since cross-validation gives you a better idea of how your model will perform on new data, it helps you adjust your model to avoid overfitting.
