gbm_pro

Fits a stochastic gradient boosting model using the Cox Partial Likelihood distribution.
Supports random search for hyperparameter optimization.

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

gbm_pro function

<dl><dt>X</dt>
<dd>A data frame of predictors.</dd>
<dt>y_surv</dt>
<dd>A <code>Surv</code> object.</dd>
<dt>tune</dt>
<dd>Logical. If TRUE, performs random search.</dd>
<dt>cv.folds</dt>
<dd>Integer. Number of cross-validation folds.</dd>
<dt>max_tune_iter</dt>
<dd>Integer. Maximum iterations for random search.</dd></dl>

Arguments

Train Gradient Boosting Machine (GBM) for Survival — gbm_pro

<dl>

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


<dt>y_surv</dt>
<dd>A <code>Surv</code> object.</dd>


<dt>tune</dt>
<dd>Logical. If TRUE, performs random search.</dd>


<dt>cv.folds</dt>
<dd>Integer. Number of cross-validation folds.</dd>


<dt>max_tune_iter</dt>
<dd>Integer. Maximum iterations for random search.</dd>

</dl>

gbm_pro: Train Gradient Boosting Machine (GBM) for Survival

Description

Usage

Value

Arguments