RandomizedSearchCV is a method provided by scikit-learn for hyperparameter tuning and model selection through cross-validation. It’s similar to GridSearchCV, but instead of exhaustively searching through all possible combinations of hyperparameters, it randomly samples a fixed number of hyperparameter settings from specified distributions. Here’s a basic overview of how RandomizedSearchCV works: Here’s a basic example of…
In scikit-learn’s GridSearchCV (Grid Search Cross Validation), the parameter cv stands for “cross-validation.” It determines the cross-validation splitting strategy to be used when evaluating the performance of a machine learning model. When cv is set to an integer (e.g., cv=5), it represents the number of folds in a (Stratified) K-Fold cross-validation. For example, cv=5 means…
Grid search is a tuning technique that attempts to compute the optimum values of hyperparameters. It is an exhaustive search that is performed on the specific parameter values of a model. The parameters of the estimator/model used to apply these methods are optimized by cross-validated grid-search over a parameter grid.
