Measuring predictive performance of data mining and artificial neural network algorithms for predicting lactation milk yield in indigenous akkaraman sheep

The aim of the investigation was to determine several factors affecting lactation milk yield amounts of indigenous akkaraman ewes reared in sanliurfa (137 farms) and hakkari (113 farms) provinces of turkey. for the determination process,the statistical predictive accuracy of glm (general linear model),cart (classification and regression tree),chaid (chi-square automatic interaction detector),exhaustive chaid,and a ann type mlp (multilayer perceptron) in the prediction of lactation milk yield in sheep was measured comparatively by model assessment criteria,such as r2,r2 adjusted,sdratio,cv(%),rmse,rae,mape,mad and pearson correlation coefficient (r) between actual and predicted milk yield values,respectively. minimum farm numbers for parent and child nodes were assigned at 10:5 for producing the best predictive accuracy in cart and both chaid algorithms. according to the criteria,the significance order in the predictive accuracy for lactation milk yield was recorded as chaid=exhaustive chaid >ann>glm >cart. the decision tree results of chaid algorithm displayed that ewe age,number of milking and lactation length for lactation milk yield should be taken into account and lactation lengths for akkaraman sheep milked more than 2 a day should be longer than 150 days at 3 year old age group,and longer than 160 days at 4 year old age group for sustaining high productivity in lactation milk yield. consequently,it is recommendable that determinant factors such as age,number of milking and lactation length on lactation milk yield should be considered on the basis of the powerful algorithm giving high predictive power. copyright © 2016 zoological society of pakistan.