ASAS-NANP Symposium: mathematical modeling in animal nutrition: construction of supervised machine learning regression pipelines for livestock data modeling: a case studys

Source abstract

Abstract Integrating open-source tools and machine learning (ML) pipelines into livestock data analysis transforms research, education, and decision-making in animal science. This study presents a comprehensive, end-to-end regression pipeline implemented in Python, designed to predict outcome variables from structured input data in livestock systems. The pipeline includes essential stages of data preparation, such as cleaning, normalization, transformation, and exploratory data analysis, followed by model development, hyperparameter tuning, and interpretability analysis. Two real-world case studies are used to demonstrate the pipeline’s adaptability and predictive capabilities in addressing domain-specific questions in livestock production. The open-source nature of the pipeline serves multiple purposes. First, it promotes reproducibility, a critical requirement in scientific research and data-intensive industry applications, by allowing others to verify and build upon the presented methodology. Second, it enhances accessibility and equity in data science education, enabling students and professionals alike to explore ML applications without the barrier of expensive software or proprietary code. Third, the pipeline is fully modular, encouraging users to adapt, integrate new ML algorithms, and extend components for tasks such as classification, clustering, or time series forecasting in livestock datasets. Beyond its technical implementation, the pipeline emphasizes interpretability, representing an often overlooked yet vital aspect of deploying ML in agricultural contexts. Through the importance of permuted features, residual analysis, and model diagnostics, users gain actionable insights into which variables drive predictions, supporting more informed decisions in herd management, nutrition planning, and breeding programs. This focus ensures that ML outputs are not just accurate, but also meaningful and aligned with real-world livestock production goals. In summary, this work contributes a versatile and transparent machine learning resource tailored for animal science applications. Making the code openly available bridges the gap between methodological advancement and practical deployment, empowering researchers, students, and practitioners to apply ML for better decision-making and scientific discovery in livestock systems.