ChatGPT said: ChatGPT For your SVM classifier, it's generally recommended to perform cross-validation on the training set, not on the entire dataset. This approach aligns with the principle of keeping your test data entirely unseen during both the training and the validation processes to avoid data leakage and ensure that your model generalizes well to new, unseen data.
Here’s a step-by-step explanation:
Split the data into training and test sets: This separation ensures that the test set acts as new, unseen data for evaluating the final model.
Perform cross-validation on the training set: Here, you apply cross-validation techniques such as K-Fold on the training data (X_train and y_train). This process helps in assessing how well your model, with a given set of hyperparameters, is expected to perform on unseen data. It allows you to tune the model without touching the test set.
Final evaluation on the test set: After choosing the best model and parameters using cross-validation on the training set, use the test set to assess the performance of the model. This step confirms the ability of your model to generalize to new data.
Thus, your second approach using X_train and y_train for cross-validation is correct.