Which hyperparameter optimization technique is more suitable for models with complex hyperparameter interactions?Review LaterGrid SearchRandom SearchBayesian OptimizationNone of the above

Which hyperparameter optimization technique can be computationally expensive if the search space is large or the model training is time-consuming?Review LaterGrid SearchRandom SearchBayesian OptimizationGenetic Algorithm

Which hyperparameter optimization technique constructs a probabilistic model of the hyperparameter space and balances exploration and exploitation?Review LaterGrid SearchRandom SearchBayesian OptimizationNone of the above

Which technique is used for both regression and classification tasks in hyperparameter optimization?Review LaterGrid SearchRandom SearchBayesian OptimizationElasticNet Regularization

16. A data analyst has trained an Extreme Gradient Boosting Algorithm using the default hyperparameters. The model obtained an accuracy of 80 percent. The analyst was advised that if she does hyperparameter tuning, she can improve the perfomance of the model. Which among the following methods can the analyst use. - i. Bayesian search- ii. Grid search- iii. Randomized searchii. onlyiii. onlyii. and iii. onlyi., ii., and iii.i. and ii. onlyi. and iii. onlyi. only

Write a function that returns the best hyperperameters for a given model (i.e. the GridSearchCV).Function specifications:Should take in an sklearn GridSearchCV object.Should return a dictionary of optimal parameters for the given model.