xb_dia

Trains an Extreme Gradient Boosting (XGBoost) model using <code><a href="/link/caret%3A%3Atrain?package=E2E&version=0.1.2" data-mini-rdoc="E2E::caret::train">caret::train</a></code>
for binary classification.

Provides a framework to build and evaluate diagnosis or
prognosis models using stacking, voting, and bagging ensemble
techniques with various base learners. The package also includes
tools for visualization and interpretation of models. The development
version of the package is available on 'GitHub' at
<https://github.com/xiaojie0519/E2E>. The methods
are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655>
on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1>
on stacking.

Shanjie Luan

Ensemble Learning Framework for Diagnostic and Prognostic
Modeling

xb_dia function

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

Arguments

Trains an Extreme Gradient Boosting (XGBoost) model using <code><a href='https://rdrr.io/pkg/caret/man/train.html'>caret::train</a></code>
for binary classification.

Train an XGBoost Tree Model for Classification — xb_dia

<dl>

<dt>X</dt>
<dd>A data frame of features.</dd>


<dt>y</dt>
<dd>A factor vector of class labels.</dd>


<dt>tune</dt>
<dd>Logical, whether to perform hyperparameter tuning using <code>caret</code>'s
default grid (if <code>TRUE</code>) or use fixed values (if <code>FALSE</code>).</dd>


<dt>cv_folds</dt>
<dd>An integer, the number of cross-validation folds for <code>caret</code>.</dd>


<dt>tune_length</dt>
<dd>An integer, the number of random parameter combinations to try
when tune=TRUE. Only used when search="random". Default is 20.</dd>

</dl>

xb_dia: Train an XGBoost Tree Model for Classification

Description

Usage

Value

Arguments