xb_dia: Train an XGBoost Tree Model for Classification

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Train an XGBoost Tree Model for Classification

Description

Trains an Extreme Gradient Boosting (XGBoost) model using caret::train for binary classification.

Usage

xb_dia(X, y, tune = FALSE, cv_folds = 5, tune_length = 20)

Arguments

X	A data frame of features.
y	A factor vector of class labels.
tune	Logical, whether to perform hyperparameter tuning using caret's default grid (if TRUE) or use fixed values (if FALSE).
cv_folds	An integer, the number of cross-validation folds for caret.
tune_length	An integer, the number of random parameter combinations to try when tune=TRUE. Only used when search="random". Default is 20.

Value

A caret::train object representing the trained XGBoost model.

E2E documentation built on March 22, 2026, 1:07 a.m.