Random Forest: "rf". Regression and classification.
Implemented via ranger.
mtry: Number of variables to consider for each split
splitrule: Splitting rule. For classification either "gini" or "extratrees". For regression either "variance" or "extratrees".
min.node.size: Minimal node size.
XGBoost: "xgb". eXtreme Gradient Boosting
Implemented via xgboost. Note that XGB has many more hyperparameters
than the other models. Because of this, it may require greater tune_depth
to optimize performance.
eta: Control for learning rate, [0, 1]
gamma: Threshold for further cutting of leaves, [0, Inf]. Larger is more conservative.
max_depth: Maximum tree depth, [0, Inf]. Larger means more complex models and so greater likelihood of overfitting. 0 produces no limit on depth.
subsample: Fraction of data to use in each training instance, (0, 1].
colsample_bytree: Fraction of features to use in each tree, (0, 1].
min_child_weight: Minimum sum of instance weight need to keep partitioning, [0, Inf]. Larger values mean more conservative models.
nrounds: Number of rounds of boosting, [0, Inf). Larger values produce a greater likelihood of overfitting.
Regularized regression: "glm". Regression and classification.
Implemented via glmnet.
alpha: Elasticnet mixing parameter, in [0, 1]. 0 = ridge regression; 1 = lasso.
lambda: Regularization parameter, > 0. Larger values make for stronger regularization.
get_supported_models()
Vector of currently-supported algorithms.
hyperparameters for more detail on hyperparameter
defaults and specifications