tutorial

MLwrap_tutorial

A comprehensive tutorial demonstrating the complete MLwrap workflow is
available. The tutorial provides detailed guidance on data preprocessing,
model building, hyperparameter tuning, model evaluation, and sensitivity
analysis across all supported machine learning algorithms (Neural Networks,
Random Forests, SVM, and XGBoost) within the Knowledge Discovery in
Databases (KDD) framework.

A minimal library specifically designed to make the estimation of Machine Learning
(ML) techniques as easy and accessible as possible, particularly within the framework of
the Knowledge Discovery in Databases (KDD) process in data mining. The package provides
essential tools to structure and execute each stage of a predictive or classification
modeling workflow, aligning closely with the fundamental steps of the KDD methodology,
from data selection and preparation, through model building and tuning, to the
interpretation and evaluation of results using Sensitivity Analysis. The 'MLwrap' workflow
is organized into four core steps; preprocessing(), build_model(), fine_tuning(), and
sensitivity_analysis(). It also includes global and pairwise interaction analysis based on
Friedman’s H-statistic to support a more detailed interpretation of complex feature
relationships.These steps correspond, respectively, to data preparation and transformation,
model construction, hyperparameter optimization, and sensitivity analysis. The user can
access comprehensive model evaluation results including fit assessment metrics, plots,
predictions, and performance diagnostics for ML models implemented through 'Neural
Networks', 'Random Forest', 'XGBoost' (Extreme Gradient Boosting), and 'Support Vector
Machines' (SVM) algorithms. By streamlining these phases, 'MLwrap' aims to simplify the
implementation of ML techniques, allowing analysts and data scientists to focus on
extracting actionable insights and meaningful patterns from large datasets, in line with
the objectives of the KDD process.

Albert Sesé

MLwrap

Machine Learning Modelling for Everyone

Javier Martínez García

Juan José Montaño Moreno

tutorial function

Available at tools:::Rd_expr_doi("10.31234/osf.io/j6m4z_v2")

Preprint

While MLwrap provides a streamlined and user-friendly interface for
implementing machine learning workflows, the underlying models represent
sophisticated algorithms with substantial theoretical and computational
complexity. The tutorial bridges this gap by explaining the rationale
behind preprocessing decisions, hyperparameter choices, and interpretation
of model outputs. Understanding these concepts ensures appropriate
application of the methods, proper interpretation of results, and awareness
of potential limitations in specific contexts.
The tutorial demonstrates practical applications through complete workflows,
helping users navigate the balance between methodological rigor and
implementation simplicity that MLwrap offers. This is particularly valuable
for researchers transitioning from traditional statistical methods to
machine learning approaches, or those seeking to ensure reproducible and
theoretically sound applications in their work.
Users are strongly encouraged to consult the tutorial for detailed examples
and best practices.

Why consult the tutorial

This paper is also interesting for ML users as it serves as a primer
for estimating ML models using Python code, particularly in the context of
Social, Health, and Behavioral research.
Martínez-García, J., Montaño, J. J., Jiménez, R., Gervilla, E.,
Cajal, B., Núñez, A., Leguizamo, F., &amp; Sesé, A. (2025).
Decoding Artificial Intelligence: A Tutorial on Neural Networks
in Behavioral Research. Clinical and Health, 36(2), 77-95.
tools:::Rd_expr_doi("10.5093/clh2025a13")

Tutorial for implementing ML with Python

MLwrap Comprehensive Tutorial — tutorial

Available at tools:::Rd_expr_doi("10.31234/osf.io/j6m4z_v2")

While MLwrap provides a streamlined and user-friendly interface for
implementing machine learning workflows, the underlying models represent
sophisticated algorithms with substantial theoretical and computational
complexity. The tutorial bridges this gap by explaining the rationale
behind preprocessing decisions, hyperparameter choices, and interpretation
of model outputs. Understanding these concepts ensures appropriate
application of the methods, proper interpretation of results, and awareness
of potential limitations in specific contexts.
The tutorial demonstrates practical applications through complete workflows,
helping users navigate the balance between methodological rigor and
implementation simplicity that MLwrap offers. This is particularly valuable
for researchers transitioning from traditional statistical methods to
machine learning approaches, or those seeking to ensure reproducible and
theoretically sound applications in their work.
Users are strongly encouraged to consult the tutorial for detailed examples
and best practices.

This paper is also interesting for ML users as it serves as a primer
for estimating ML models using Python code, particularly in the context of
Social, Health, and Behavioral research.
Martínez-García, J., Montaño, J. J., Jiménez, R., Gervilla, E.,
Cajal, B., Núñez, A., Leguizamo, F., &amp; Sesé, A. (2025).
Decoding Artificial Intelligence: A Tutorial on Neural Networks
in Behavioral Research. Clinical and Health, 36(2), 77-95.
tools:::Rd_expr_doi("10.5093/clh2025a13")

tutorial: MLwrap Comprehensive Tutorial

Description

Usage

Arguments

Value

Preprint

Why consult the tutorial

Tutorial for implementing ML with Python

Details

Examples