get_default_tune_params: Get Default Tuning Parameters

In fastml: Fast Machine Learning Model Training and Evaluation

Get Default Tuning Parameters

Description

Returns a list of default tuning parameter ranges for a specified algorithm based on the provided training data, outcome label, and engine.

Usage

get_default_tune_params(algo, train_data, label, engine)

Arguments

algo	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".
train_data	A data frame containing the training data.
label	A character string specifying the name of the outcome variable in train_data. This column is excluded when calculating the number of predictors.
engine	A character string specifying the engine to be used for the algorithm. Different engines may have different tuning parameter ranges.

Details

The function first determines the number of predictors by removing the outcome variable (specified by label) from train_data. It then uses a switch statement to select a list of default tuning parameter ranges tailored for the specified algorithm and engine. The tuning ranges have been adjusted for efficiency and may include parameters such as mtry, trees, min_n, and others depending on the algorithm.

Value

A list of tuning parameter ranges for the specified algorithm. If no tuning parameters are defined for the given algorithm, the function returns NULL.

fastml documentation built on June 11, 2025, 1:07 a.m.