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Updated: Jan 13, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

The importance of choosing a proper validation strategy in predictive models. Part 2: Recipes for (avoiding)

Eneko Lopez¹, Giulia Gorla², Jaione Etxebarria-Elezgarai³

¹CIC nanoGUNE BRTA, Tolosa Hiribidea 76, San Sebastián, 20018, Spain; Department of Physics, University of the Basque Country (UPV/EHU), San Sebastián, 20018, Spain.

Analytica Chimica Acta

|January 9, 2026

Summary

Overfitting in predictive modeling is often caused by poor validation and data issues, not just complexity. This study identifies common pitfalls and offers guidelines for trustworthy, generalizable models.

Keywords:

Chemometrics Cross-validation Feature selection Hyperparameter tuning Overfitting Reproducibility Validation

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Area of Science:

Machine Learning
Predictive Modeling
Data Science

Background:

Overfitting is a significant challenge in predictive modeling, leading to poor generalization.
It's often misattributed solely to model complexity, masking other critical issues.

Purpose of the Study:

To identify overlooked causes of overfitting beyond model complexity.
To provide practical guidelines for robust validation and trustworthy predictive models.

Main Methods:

Analysis of common practices contributing to overfitting.
Examination of data leakage and biased model selection.
Review of publication pressures leading to overoptimization.

Main Results:

Inadequate validation strategies are a primary driver of overfitting.
Data preprocessing flaws and biased selection inflate apparent accuracy.
Publication incentives can encourage result-driven overoptimization.

Conclusions:

Addressing validation, preprocessing, and selection biases is crucial for reliable models.
Researchers need practical guidelines to ensure model trustworthiness and generalizability.
This work provides a blueprint for reproducible and robust predictive modeling.