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One of the most used functions to do Hyperparameter Tuning with k-fold cross-validation is GridSearchCV.

GridSearchCV performs k-fold cross-validation, specified by the cv parameter. If the cv parameter is an integer, it represents the number of folds for k-fold cross-validation.

GridSearchCV loops through each value of a predefined hyperparameter and fits your model on your training set.

So, in the end, you can select the best parameters from the listed hyperparameters. This makes the processing time-consuming and expensive based on the number of hyperparameters involved.

? Blog - https://rohan-paul-ai.netlify.app/blog/post/implementing-custom-gridsearchcv-from-scratch-without-scikitlearn

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? Link To Previous Video - What K-Fold Cross Validation really is in Machine Learning - https://youtu.be/6UfvNkRoRCU

? Link To Video - RandomizedSearchCV From Scratch without scikit-learn - https://www.youtube.com/watch?v=8YJRtyx10vw&list=PLxqBkZuBynVRMORlFw95iNTp9aZzmmz4Y&index=3

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