How can you tune the hyper parameters of XGboost,Random Forest,SVM algorithm?

Question

Accepted Answer

Hyperparameters of XGBoost, Random Forest, and SVM can be tuned using techniques like grid search, random search, and Bayesian optimization.

For XGBoost, important hyperparameters to tune include learning rate, maximum depth, and number of estimators.
For Random Forest, important hyperparameters to tune include number of trees, maximum depth, and minimum samples split.
For SVM, important hyperparameters to tune include kernel type, regularization parameter, and gamma value.
Grid search involves exhaustively trying all possible combinations of hyperparameters.
Random search randomly selects combinations of hyperparameters to evaluate.
Bayesian optimization uses probabilistic models to find the best hyperparameters.
Cross-validation is commonly used to evaluate the performance of different hyperparameter settings.

Accepted Answer

We will either use random search cv or grid search cv to find the best parameters to work, with above algorithms

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