Training Folder

We applied a random forest algorithm to process accelerometer data from broiler chickens. Data from three broiler strains at a range of ages (from 25-49 days old) were used to train and test the algorithm and, unlike other studies, the algorithm was further tested on an unseen broiler strain. When tested on unseen birds from the three training broiler strains the random forest model classified behaviours with very good accuracy (92%), specificity (94%) and good sensitivity (88%) and precision (88%). With the new, unseen strain the model classified behaviours with very good accuracy (94%), sensitivity (91%), specificity (96%) and precision (91%).

本研究采用随机森林（random forest）算法处理肉鸡的加速度计数据。本研究选取3个肉鸡品系在25至49日龄区间内采集的数据，用于该算法的训练与测试；与现有同类研究不同的是，本研究额外将算法在一个未纳入训练集的肉鸡品系上进行了性能验证。当在来自3个训练用肉鸡品系的未参与训练的个体上开展测试时，该随机森林模型对肉鸡行为的分类表现优异：准确率达92%，特异性（specificity）为94%，灵敏度（sensitivity）与精确率（precision）均为88%。而在该全新未观测肉鸡品系上进行测试时，模型对肉鸡行为的分类表现同样出色，准确率达94%、灵敏度为91%、特异性为96%、精确率为91%。