Random Forest: "rf". Regression and classification. Implemented via ranger.

  • mtry: Fraction of variables to consider for each split

  • splitrule: Splitting rule. For classification either "gini" or "extratrees". For regression either "variance", "extratrees", or "maxstat".

  • min.node.size: Minimal node size.

k-nearest neighbors: "knn". Regression and classification. Implemented via kknn.

  • kmax: Number of neighbors to consider.

  • distance: Minkowsky distance parameter, (0, Inf). 1 = Manhatten, 2 = Euclidian, -> Inf = Chebyshev.

  • kernal: Kernal to use. Possible choices are "rectangular" (standard knn), "triangular", "epanechnikov", "biweight", "triweight", "cos", "inv", "gaussian", "rank", or "optimal".

get_supported_models()

Value

Vector of currently-supported algorithms.

See also

hyperparameters for more detail on hyperparameter defaults and specifications