Avoiding common machine learning pitfalls.

Area of Science:

• Computer Science
• Artificial Intelligence
• Data Science

Background:

• Machine learning (ML) practice is prone to errors, potentially eroding trust in ML-derived findings and products.
• Academic research using ML requires rigorous comparisons and valid conclusions, areas often susceptible to common mistakes.

Purpose of the Study:

• To outline frequent errors in machine learning practice.
• To provide guidance on avoiding these mistakes throughout the ML lifecycle.
• To enhance the reliability and validity of machine learning applications in academic research.

Main Methods:

• The tutorial covers five key stages of the machine learning process.
• Pre-model building considerations.
• Reliable model construction techniques.
• Robust model evaluation strategies.
• Fair model comparison methodologies.
• Effective results reporting standards.

Main Results:

• Identification of common mistakes across the ML workflow.
• Strategies for mitigating errors in model building and evaluation.
• Guidelines for ensuring fair model comparisons.
• Best practices for transparent and accurate reporting of ML results.

Conclusions:

• Avoiding common mistakes is crucial for maintaining confidence in machine learning.
• Implementing the outlined practices enhances the rigor and validity of academic ML research.
• This tutorial serves as a practical guide for researchers to improve their ML methodology.