Regression tree analysis for predicting slaughter weight in broilers

-The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF) in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth) and testicular (testicular length, scrotal length, and scrotal circumference) measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1) and with interaction (MARS_2) terms. The superiority order in the predictive accuracy of the algorithms was found as CART > CHAID ≈ Exhaustive CHAID > MARS_2 > MARS_1 > RBF > MLP. Moreover, all tested algorithms provided a strong predictive accuracy for estimating body weight. However, MARS is the only algorithm that generated a prediction equation for body weight. Therefore, it is hoped that the available results might present a valuable contribution in terms of predicting body weight and describing the relationship between the body weight and body and testicular measurements in revealing breed standards and the conservation of indigenous gene sources for Mengali sheep breeding. Therefore, it will be possible to perform more profitable and productive sheep production. Use of data mining algorithms is useful for revealing the relationship between body weight and testicular traits in describing breed standards of Mengali sheep.

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“…Data mining algorithms were more advantageous about the violation of the assumption regarding input variables (Mendes and Akkartal, 2009). …”

Section: Discussionmentioning

confidence: 99%

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

et al. 2017

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“…In fact, making more accurate decision on preferring the most effective statistical methods is the most important matter in the estimation of EW from egg quality traits. In comparison with these statistical methods highlighted above, it was said that RTM, which can be understood and interpreted more easily in visual form, was not influenced by multicollinearity, outliers, and missing observations (Mendes and Akkartal, 2009;Karabağ et al, 2010). However, the results depicted that no multicollinearity problem was detected in either MLR or RR analyses.…”

Section: Discussionmentioning

confidence: 99%

“…There is a limited availability of RTM in the poultry science (Mendes and Akkartal, 2009). Taking into consideration earlier studies concerning RTM and its advantages, RTM based on CHAID algorithm can be an admirable tool in the classification of eggs, existing in egg quality criteria instead of traditional regression methods.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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-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.

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“…The tree is constructed by splitting the whole data into nodes or sub-groups by using all the independent variables. This process goes on until the requirements of homogeneity are met on any child node [13,14] . It is aimed with obtaining terminal nodes in order to increase proportion of variance among nodes [15] .…”

Section: Methodsmentioning

confidence: 99%

CART ve CHAID Algoritmalarının Karayaka Koyun Islahında Kullanımı

Olfaz¹,

Tirink²,

Önder³

2018

Kafkas Univ Vet Fak Derg

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The aim of this study was to determine the effect of some factors (sex, birth type, farm type, birth weight and weighting time) on weaning weight through CART and CHAID data mining algorithms. The classification and regression trees are modern analytic techniques that construct tree-based data-mining algorithms. Regression trees are used for the purpose of preliminary selection of the traits affecting the continuous dependent variable. The studied data were consisted of 366 records from Karayaka sheep breed. The CHAID algorithms results revealed that; predictors such as weighting time, sex and farm type statistically influenced weaning weight Regression tree diagram constructed by CART algorithm depicted that birth type was effect the weaning weight, and in this tree weighting time of single born lambs was affected the birth type. The predicted values and original values were correlated (P<0.05). As a result, it could be suggested that CHAID algorithm was found more useful biologically than CART. CART ve CHAID Algoritmalarının Karayaka Koyun Islahında KullanımıÖz Bu çalışma, sütten kesim ağırlığı üzerime bazı faktörlerin (cinsiyet, doğum tipi, işletme tipi, doğu ağırlığı ve ölçüm zamanı) CART ve CHAID veri madenciliği algoritmaları ile belirlenmesini amaçlamaktadır. Sınıflandırma ve regresyon ağaçları veri madenciliği kapsamında olan modern analitik yöntemler sınıfında yer almaktadır. Regresyon ağaçları, sürekli bağımlı değişkeni etkileyen özelliklerin ön seçimi amacıyla kullanılmaktadır. Çalışmada Karayaka koyun ırkına ait 366 kayıt veri olarak kullanılmıştır. Sonuç olarak; CHAID algoritmasına göre ölçüm zamanı, cinsiyet ve işletme tipi sütten kesim ağırlığı üzerinde önemli derecede etkili bulunmuştur. CART algoritmasına ait sonuçlar ise sütten kesim ağırlığı üzerine doğum tipinin etkili olduğunu göstermiştir. Bu ağaçta tekiz kuzuların ölçüm zamanının doğum tipinden etkilendiği anlaşılmıştır. Tahmin edilen ve gözlenen değerler yüksek ilişkili bulunmuştur (P<0.05). Sonuç olarak, CHAID algoritmasının CART algoritmasına göre biyolojik olarak daha kullanışlı olduğu belirlenmiştir.

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Regression tree analysis for predicting slaughter weight in broilers

Cited by 41 publications

References 21 publications

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

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

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

CART ve CHAID Algoritmalarının Karayaka Koyun Islahında Kullanımı

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