Table 3.

A summary of the classification model performance in predicting cow BCS categories

Classifier class	Model name	Validation accuracy, %	Validation total cost	Test accuracy, %	Test total cost
Discriminant	Linear discriminant	41.86	25	20	8
Ensemble	Boosted trees	44.19	24	40	6
	Bagged trees	58.14	18	70	3
	Subspace discriminant	41.86	25	40	6
	Subspace KNN	41.86	25	50	5
	RUSBoosted trees	37.21	27	40	6
Kernel	SVM kernel	46.51	23	50	5
	Logistic regression kernel	44.19	24	50	5
k-nearest neighbor (KNN)	Fine KNN	44.19	24	20	8
	Medium KNN	32.56	29	40	6
	Coarse KNN	44.19	24	40	6
	Cosine KNN	34.88	28	20	8
	Cubic KNN	37.21	27	30	7
	Weighted KNN	44.19	24	30	7
Naive Bayes	Kernel Naïve Bayes	39.53	26	40	6
Neural network	Narrow neural network	41.86	25	10	9
	Medium neural network	48.84	22	40	6
	Wide neural network	34.88	28	20	8
	Bilayered neural network	37.21	27	50	5
	Trilayered neural network	39.53	26	50	5
Supported vector machine (SVM)	Cubic SVM	41.86	25	40	6
	Fine Gaussian SVM	44.19	24	40	6
	Medium Gaussian SVM	46.51	23	30	7
	Coarse Gaussian SVM	44.19	24	40	6
	Linear SVM	51.16	21	20	8
	Quadratic SVM	44.19	24	30	7
Tree	Fine tree	44.19	24	80	2
	Medium tree	44.19	24	80	2
	Coarse tree	44.19	24	70	3

The classifier class, model name, and performance parameters, including the accuracy of the validation and test (%) and the total cost in validation and test are provided. The general information of these classifiers is listed in Table 1. The best model is highlighted in italic.