Hyperparameter Tuning in R

It is not easy to just run a machine-learning problem from the box and make a prediction. The best model must accurately predict the outcome. You can improve your model by hyperparameter tuning. This is the process of optimizing your model's settings. This course will show you how to use caret, mlr and h2o packages to find the optimal combination of hyperparameters. This course uses grid search and random searching as well as adaptive resampling and automated machine learning (AutoML). You can also tune different supervised models like support vector and gradient boosting machines. Tune up!