Returns a list of default tuning parameters for the specified algorithm based on the task type, number of predictors, and engine.
get_default_params(algo, task, num_predictors = NULL, engine = NULL)A list of default parameter settings for the specified algorithm. If the algorithm is not recognized, the function returns NULL.
A character string specifying the algorithm name. Supported values include:
"rand_forest", "C5_rules", "xgboost", "lightgbm",
"logistic_reg", "multinom_reg", "decision_tree", "svm_linear",
"svm_rbf", "nearest_neighbor", "naive_Bayes", "mlp",
"deep_learning", "discrim_linear", "discrim_quad", "bag_tree",
"elastic_net", "bayes_glm", "pls", "linear_reg",
"ridge_regression", and "lasso_regression".
A character string specifying the task type, typically "classification" or "regression".
An optional numeric value indicating the number of predictors. This value is used to compute default values for parameters such as mtry. Defaults to NULL.
An optional character string specifying the engine to use. If not provided, a default engine is chosen where applicable.
The function employs a switch statement to select and return a list of default parameters tailored for the given algorithm, task, and engine. The defaults vary by algorithm and, in some cases, by engine. For example:
For "rand_forest", if engine is not provided, it defaults to "ranger". The parameters such as mtry, trees, and min_n are computed based on the task and the number of predictors.
For "C5_rules", the defaults include trees, min_n, and sample_size.
For "xgboost" and "lightgbm", default values are provided for parameters like tree depth, learning rate, and sample size.
For "logistic_reg" and "multinom_reg", the function returns defaults for regularization parameters (penalty and mixture) that vary with the specified engine.
For "decision_tree", the parameters (such as tree_depth, min_n, and cost_complexity) are set based on the engine (e.g., "rpart", "C5.0", "partykit", "spark").
Other algorithms, including "svm_linear", "svm_rbf", "nearest_neighbor", "naive_Bayes", "mlp", "deep_learning", "elastic_net", "bayes_glm", "pls", "linear_reg", "ridge_regression", and "lasso_regression", have their respective default parameter lists.