HMDA (version 0.1.1)

hmda.suggest.param: Suggest Hyperparameters for tuning HMDA Grids

Description

Suggests candidate hyperparameter values for tree-based algorithms. It computes a hyperparameter grid whose total number of model combinations is near a specified target. For GBM models, default candidates include max_depth, ntrees, learn_rate, sample_rate, and col_sample_rate. For DRF models, if a vector of predictor variables (x) and a modeling family ("regression" or "classificaiton") are provided, a vector of mtries is also suggested.

Usage

hmda.suggest.param(algorithm, n_models, x = NULL, family = NULL)

Value

A named list of hyperparameter value vectors. This list is suitable for use with HMDA and H2O grid search functions.

Arguments

algorithm

A character string specifying the algorithm, which can be either "gbm" (gradient boosting machines) or "drf" (distributed random forest).

n_models

An integer for the desired approximate number of model combinations in the grid. Must be at least 100.

x

(Optional) A vector of predictor names. If provided and its length is at least 20, it is used to compute mtries for DRF.å

family

(Optional) A character string indicating the modeling family. Must be either "classification" or "regression". This is used with x to suggest mtries.

Details

The function first checks that n_models is at least 100, then validates the family parameter if provided. The algorithm name is normalized to lowercase and must be either "gbm" or "drf". For "gbm", a default grid of hyperparameters is defined. For "drf", if both x and family are provided, the function computes mtries via suggest_mtries(). If not, a default grid is set without mtries. Finally, the candidate grid is pruned or expanded using hmda.adjust.params() so that the total number of combinations is near n_models.

Examples

if (FALSE) {
  library(h2o)
  h2o.init()

  # Example 1: Suggest hyperparameters for GBM with about 120 models.
  params_gbm <- hmda.suggest.param("gbm", n_models = 120)
  print(params_gbm)

  # Example 2: Suggest hyperparameters for DRF (classification) with
  # 100 predictors.
  params_drf <- hmda.suggest.param(
                  algorithm = "drf",
                  n_models = 150,
                  x = paste0("V", 1:100),
                  family = "classification"
                )
  print(params_drf)
}