min_max_normalize

Performs linear transformation of data to the range 0 to 1.
Essential for stacking ensembles to normalize risk scores from heterogeneous base learners.

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

min_max_normalize function

<dl><dt>x</dt>
<dd>A numeric vector.</dd>
<dt>min_val</dt>
<dd>Optional reference minimum value (e.g., from training set).</dd>
<dt>max_val</dt>
<dd>Optional reference maximum value (e.g., from training set).</dd></dl>

Arguments

Min-Max Normalization — min_max_normalize

<dl>

<dt>x</dt>
<dd>A numeric vector.</dd>


<dt>min_val</dt>
<dd>Optional reference minimum value (e.g., from training set).</dd>


<dt>max_val</dt>
<dd>Optional reference maximum value (e.g., from training set).</dd>

</dl>

min_max_normalize: Min-Max Normalization

Description

Usage

Value

Arguments