Additional file 2 of Generalized Estimating Equations Boosting (GEEB) machine for correlated data

Additional file 2. This research also provides the code that computes all research results. The geebm() is an R function that implements the GEEB machine. This function has seven arguments: formula, id, iteration, feature_rate, lrate, standardize, and data. Note that formula must be specified in the format "response ~ predictors" to list the predictors (input features) and response variable (output feature) in the dataset. id is a vector that identifies the clusters and can support multiple levels arranged in the order of multilayer structure. iteration is an integer representing the number of iterations, set to default at 100 iterations. feature_rate represents the proportion of random feature selection. When set to 1, it uses all features; by default, it is set to 0.5, using half of the features. lrate is a hyperparameter for the learning rate, with a default value of 0.1. standardize determines whether features are standardized, and the default does not perform standardization. data is used to input the training dataset. For example, when training the model with the Forest Fire Data in this study, the function would be: geebm(area~X+Y+FFMC+DMC+DC+ISI+temp+RH+wind+rain+day, id=c("season","month"), iteration=100, feature_rate=0.5, lrate=0.1, standardize=T, data=Dataset).