[Machine Learning] Model Selection - K-Fold Cross Validation | Grid Search

[ K-Fold Cross Validation ]

K-Fold Cross Validation : a validation technique used to evaluate the performance of a model. It involves dividing the given dataset into K different subsets (or folds), sequentially using each subset as the validation dataset, and using the remaining subsets as the training dataset.

 

 

 

 

 

[ Grid Search ]

 

Grid Search  : Grid Search method involves training and evaluating the model for every possible combination of hyperparameter values to find the optimal combination.

- For each hyperparameter combination, the model is trained and evaluated using a validation dataset or cross-validation. Performance metrics such as accuracy, F1 score, or log loss are commonly used to evaluate the model's performance.

=> Based on the performance metrics obtained during the validation process, the hyperparameter combination that shows the best performance is selected as the optimal choice.