K-Fold Cross-validation

Hi everyone,

Could you kindly explain whether we should do a train-test split before K-Fold cross-validation (for hyper-parameter tuning) ? How do we evaluate the model and use it to make predictions? Thank you for your time and attention.

Hi @Khai_Lap

It’s very important to performing a train-test split before diving into K-Fold cross-validation. The rationale behind this practice is maintaining a pristine test set that the model has never encountered during hyperparameter tuning. This guarantees an impartial evaluation of the final model’s performance on unseen data. K-Fold cross-validation, on the other hand, serves the purpose of model selection and hyperparameter tuning exclusively on the training data, safeguarding the test set for the ultimate assessment.

Within K-Fold cross-validation, the model’s performance undergoes evaluation on each validation fold. This entails utilizing diverse evaluation metrics depending on the specific problem at hand. For instance, classification might involve accuracy, precision, recall, or F1-score, while regression could employ metrics like mean squared error or mean absolute error.

Once the hyperparameters have been fine-tuned via K-Fold cross-validation, the next step is to train the final model using the complete training dataset, which includes all the K folds. The hyperparameters that exhibited the best performance during cross-validation are applied during this training. After this training phase, the final model is primed for making predictions on fresh, unseen data.

In essence, the amalgamation of train-test splitting and K-Fold cross-validation ensures a robust evaluation of the model’s performance, aiding in the selection of the optimal hyperparameters for your specific machine learning algorithm.

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