Comparison of Tree‐Based Machine Learning Algorithms for Classification of Livestock Breeds Based On Post‐Thaw Spermatological Parameters

Reproductive efficiency is a crucial determinant of livestock productivity, with sperm quality being a key factor in successful fertilization. The quantitative assessment of spermatozoa using computer‐assisted sperm analysis (CASA) yields valuable kinetic variables that can vary across cattle breeds. This study aimed (i) to classify post‐thawed semen samples from Holstein, Simmental and Charolais bulls based on eight CASA‐derived variables, progressive motility (PM), non‐PM, velocity curve linear (VCL), velocity straight line (VSL), beat‐cross frequency (BCF), amplitude of lateral head displacement (ALH), hyperactivity and velocity average path (VAP); (ii) to benchmark three tree‐based classifiers, C5.0, random forest (RF) and stochastic gradient boosting (SGB), for their ability to assign ejaculates to the correct breed; and (iii) to identify the most informative predictors for breed discrimination within the algorithms. We applied and compared the predictive performance of three tree‐based classification algorithms: C5.0, RF and SGB after the original dataset was randomly divided into the training and testing sets with 70%–30%, 75%–25% and 80%–20% ratios, respectively. Parameter tuning was carried out with the application of a 10‐fold cross‐validation technique with ten times repetition. The results showed that SGB achieved the highest performance for classification, with a mean balanced accuracy of 85.7% (86.4% for Holstein, 84.3% for Simmental and 86.5% for Charolais), followed by RF (83.5%) and C5.0 (73.5%). PM, hyperactivity and VSL were the most informative predictors. The results offer insights into breed‐specific sperm characteristics, with potential implications for the development of breed‐specific calibrations for CASA and ensure more efficient resource allocation in livestock production.