Inside 'How to avoid machine learning pitfalls: a guide for academic researchers', you'll find key guidelines for deploying machine learning techniques without errors. This resource is packed with insightful advice on understanding data, planning deployment, building reliable models, evaluating models, reporting results and much more. Tips are delivered through a simple, structured format with an emphasis on fair and ethical practices. A perfect guide to navigate the complex landscape of machine learning and achieve the best results.
Understanding the Dataset Before Building Models
Explore the importance of understanding your dataset before building machine learning models to avoid common pitfalls.
Preventing Overfitting and Data Leakage During Model Building
Explore guidelines for academic researchers on avoiding pitfalls in machine learning, including overfitting and data leakage during model building.
Applying Reliable Evaluation Metrics for Robust Model Evaluation
Learn how to leverage robust evaluation metrics for effective model assessment in the realm of machine learning.
Conducting Fair and Rigorous Model Comparisons
The e-book provides guidance on avoiding pitfalls in machine learning, emphasizing fair and rigorous model comparisons in academic research.
Accurate and Transparent Reporting of Results
Explore guidelines for reliable model building, robust evaluation, fair comparison and transparent reporting in machine learning research.
Planning for Deployment from the Early Stages
Gain insights on avoiding common machine learning pitfalls by focusing on deployment planning right from the early stages of model building.
