Prediction of egg weight from egg quality characteristics via ridge regression and regression tree methods

Abstract

-This study was conducted on 2049 eggs, collected from commercial white layer hybrids, with the purpose of predicting egg weight (EW) from egg quality characteristics such as shell weight (SW), albumen weight (AW), and yolk weight (YW). In the prediction of EW, ridge regression (RR), multiple linear regression (MLR), and regression tree analysis (RTM) methods were used. Predictive performance of RR and MLR methods was evaluated using the determination coefficient (R 2 ) and variance inflation factor (VIF). R 2 (%) coefficients for RR and MLR methods were found as 93.15% and 93.4% without multicollinearity problems due to very low VIF values, varying from 1 to 2, respectively. Being a visual, non-parametric analysis technique, regression tree method (RTM) based on CHAID algorithm performed a very high predictive accuracy of 99.988% in the prediction of EW. The highest EW (71.963 g) was obtained from eggs with AW > 41 g and YW > 17 g. The usability of RTM due to a very great accuracy of 99.988 (%R 2 ) in the prediction of EW could be advised in practice in comparison with the ridge regression and multiple linear regression analysis techniques, and might be a very valuable tool with respect to quality classification of eggs produced in the poultry science.

The prediction of live weight of hair goats through penalized regression methods: LASSO and adaptive LASSO

“…The highest correlation coefficient with LW was revealed by CG for both genders. This was in agreement with the finding of previous studies (Pesmen and Yardimci, 2008;Cam et al, 2010;Tsegaye et al, 2013;Das and Yadav, 2015;Sam et al, 2016). The present study was focused the correlations between explanatory variables.…”

“…Multiple linear regression (MLR), based on ordinary least squares (OLS), is a traditional, simple method that has been used by researchers in order to predict the complex relationship between live weight and some body measurements in goat, sheep, cattle, fish, etc. (Francis et al, 2002;Pesmen and Yardimci, 2008;Yılmaz et al, 2013). However, when a multicollinearity problem exists among explanatory variables, the OLS method produces poor predictions (Montgomery et al, 2001;Yakubu, 2010;Dormann et al, 2013;Khan et al, 2014).…”

Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan

“…Descriptive statistics of the input and output variables are given in Table 1. CART, CHAID, and Exhaustive CHAID are visual algorithms that create regression tree structures and analyze qualitative and quantitative data simultaneously. CHAID (Kass, 1980) and Exhaustive CHAID (Biggs et al, 1991) three-stage-data mining algorithms (merging, partitioning, and stopping) are tree-based algorithms that recursively use multi-way splitting to form homogenous subsets on the basis of Bonferroni adjustment until the differences between the actual and the predicted values in output variable are minimal (Orhan et al, 2016;Akin et al, 2016;Akin et al, 2017;Eyduran et al, 2016). A quantitative input variable in CHAID algorithms is converted into an ordinal variable (Orhan et al, 2016).…”

Regression tree analysis for predicting body weight of Nigerian Muscovy duck (Cairina moschata)

“…Conversely, the application of this technique to poultry is limited. Of recent, MENDES and AKKARTAL (2009) used RTA in predicting slaughter weight of broilers from two-week linear body parameters while UCKARDES et al 2014and ORHAN et al (2016) respectively, applied it to predict egg weight of chicken from egg characteristics and to identify genetic and non-genetic factors influencing fertility of quail eggs.…”