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Bees are the main pollinators of most insect-pollinated wild plant species and are essential for the maintenance of plant ecosystems and food production. However, over the past three decades they have been suffering from numerous health challenges, including changes in habitat, pollutants and toxins, pests and diseases, and competition for resources. An attempt to mitigate this problem is to estimate the health status of colonies and indicate an imminent collapsing state to beekeepers. To estimate the health status of bee colonies, we propose a method for calibrating a classification algorithm based on a supervised machine learning approach. We trained, validated, and tested three well-known and distinguished classification algorithms (k-Nearest Neighbors, Random Forest, and Neural Networks) and used real datasets from 6 apiaries, 27 Western honeybee (Apis mellifera) beehives monitored over three years …