Hyperparameter Tuning with Random Forest

Hyperparameter tuning is critical for building an accurate and reliable random forest model. In random forest models, some of the parameters that are considered hyperparameters and can be tuned include:

• mtry: This is the number of features to consider at each split. Typically, this is set to the square root of the total number of features, but it can be tuned to improve performance.

• ntree: This is the number of trees to include in the forest. Increasing the number of trees can improve performance but can also increase computation time.

• max_depth: This is the maximum depth of each tree. Setting a maximum depth can prevent overfitting and improve generalization performance.

• min_n: This is the minimum number of samples required to split an internal node. This can also help prevent overfitting by limiting the tree size.

• sample_size: This is the size of the bootstrap sample used to build each tree. This can be tuned to balance the bias-variance trade-off.

There are several ways to tune hyperparameters in tidymodels, including the tune function and the ability to resample our datasets. Here are some standard methods for tuning hyperparameters in random forest models:

• Grid search: Grid search is a brute-force method for hyperparameter tuning that involves specifying a range of values for each hyperparameter and then training a model for each combination of hyperparameters. The model with the best performance on a holdout set is then selected as the final model. Grid search can be time-consuming, but it’s a reliable and straightforward method that can work well for small ...