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Prediction of Egg Weight Using MARS data mining Algorithm through R

Year 2021, Volume: 24 Issue: 1, 242 - 251, 28.02.2021

Demet Çanga , Esra Yavuz , Ercan Efe

https://doi.org/10.18016/ksutarimdoga.vi.716880
Cited By: 3

Abstract

Internal and external quality characters of poultry eggs are quitely important to determine egg weight. Also, the quality of eggs is important for both hatching and egg production. The purpose of this study was modelling of egg weight with the MARS (Multivariate Adaptive Regression Splines) method using inner and outsider quality characters of egg in Lohmann LSL Classic white hybrit flock. For this purpose, the eggs of the Lohmann LSL Classic white hybrid flock (n=60) were used. Weekly egg yields were evaluated from the 22nd week to the 62nd week. In the research, for the prediction of dependent and continuous variable egg weight; shape index (SI), shell breaking resistance (SBS), shell weight (SW), shell thickness (ST), yolk diameter (YD), yolk width (YW), yolk height (YH), color (YC ), albumen length (AW), albumen height (AL) and albumen height (AH) were used. In order to obtain perfect goodness of fit, in the “earth” package of the R program, the definitions of penalty -1, degree = 2, nprune = 10 and nk = 60. The research, the mars prediction model was determined such as EW = 63.1-0.906 * max (0,75-SI)-0.32 * max (0, SI-75) -62.4 * max (0,0.57-ST) -354 * max (0, ST-0.57) + 1.13 * Groupa2 * max (0, 75-SI) + 1.49 * (0.0.57-ST) max * YD + 8.2*max(0, ST 0.57) * YD-0.02*(0 YD-38.5)max* YC-0.0366*YH * max(0,13-YC). As a result, some quality variables were found to be important in determining egg weight. Variables such as group a2, SI, YC, ST, YD, YH to estimate the weight of the egg determined as the dependent variable were used. Other variables are not included in this equation. In the poultry, the MARS prediction model may be a better alternative to classical nonlinear models in predicting egg weight since that it is easier and has higher accuracy.

Keywords

MARS, Egg weight, Lohmann bread, Goodness of fit, Non-parametric regression model

References

  • Akin M, Eyduran, SP, Eyduran E 2020. Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. Plant Cell Tiss Organ Cult 140, 661–670.
  • Aksoy A, Erturk YE, Eyduran E and Tariq MM 2018a. Comparing predictive performances of MARS and CHAID algorithms for defining factors affecting final fattening live weight in cultural beef cattle enterprises. Pakistan Journal of Zoology, 50(6): 2279-2286.
  • Aksoy A, Erturk YE, Eyduran E, Tariq MM 2018b. Utility of MARS Algorithm for Describing Non-Genetic Factors Affecting Pasture Revenue of Morkaraman Breed And Romanov × Morkaraman F1 Crossbred Sheep Under Semi İntensive Conditions. Pakistan Journal of Zoology, 51(1):235-240.
  • Aytekin I, Eyduran E, Karadas K, Akşahan R, Keskin I 2018. Prediction of Fattening Final Live Weight from Some Body Measurements and Fattening Period in Young Bulls Of Crossbred And Exotic Breeds Using Mars Data Mining Algorithm. Pakistan Journal of Zoology, 50(1):189-195.
  • Banks DL, Olszewski RT, Maxion RA 2003. Comparing Methods for Multivariate Adaptive Regression. Communication in Statistics-Simulation and Computation, 32(2):541-571. [Electronic Journal], http://www.informaworld.com/smpp/title~content=t713597237.
  • Banks DL. 2001. Exploratory Data Analysis: Multivariate Approaches (Nonparametric Reg-ression). In: International Encyclopedia of the Social & Behavioral Sciences. Eds: Smelser NJ, Baltes PB. Vol 8, 2nd ed, Elsevier, Amsterdam, p 5164-5169.
  • Banks DL. 2001. Exploratory Data Analysis: Multivariate Approaches (Nonparametric Reg-ression). In: International Encyclopedia of the Social & Behavioral Sciences. Eds: Smelser NJ, Baltes PB. Vol 8, 2nd ed, Elsevier, Amsterdam, p 5164-5169.
  • Canga D, Boga M 2019. Hayvancılıkta Mars Kullanımı Ve Bır Uygulama. III. International Scientific and Vocational Studies Congress – Science and Health 27-30 June 2019, Ürgüp, Nevşehir / Turkiye.
  • Celik S, Eyduran E, Kardaş K, Tariq MM. 2017. Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan. Revista Brasileira de Zootecnia, 46(11): 863-872.
  • Celik S, Yilmaz O. 2018. Prediction of Body Weight of Turkish Tazi Dogs using Data Mining Techniques: Classification and Regression Tree (CART) and Multivariate Adaptive Reg-ression Splines (MARS). Pakistan Journal of Zoology, 50(2): 575-583 doi:10.17582/ journal.pjz/2018.50.2.575.583
  • Celik S. 2019. Comparing Predictive Performances of Tree-Based Data Mining Algorithms and MARS Algorithm in the Prediction of Live Body Weight from Body Traits in Pakistan Goats. Pakistan Journal of Zoology, 51(4):1447.
  • Celik, S. and Boydak E. 2020. Description of The Relationships Between Different Plant Characteristics in Soybean Using Multivariette Adaptıive Regression Splines (Mars) Algorithm. Japs, Journal of Animal and Plant Sciences, 30(2): 431-441.
  • Deconinck E, Xu QS, Put R, Coomans D, Massart DL, Heyden YV 2005. Prediction ofgastro-intestinal absorption using multivariate adaptive regression splines. Journal of Pharmaceutical and Biomedical Analysis, 39: 1021-1030.
  • Erturk YE, Aksoy A, Tariq MM 2018. Effect of Selected Variables Identified by MARS on Fattening Final Live Weight of Crossbred Beef Cattle in Eastern Turkey. Pakistan Journal of Zoology, 50(4):1403-1412.
  • Eyduran E, Zaborski D, Waheed A, Celik S, Karadas, K, Grzesiak W 2017a. Comparison of the predictive capabilities of several data mining algorithms and multiple linear regression in the prediction of body weight by means of body measurements in the indigenous Beetal goat of Pakistan. Pakistan Journal of Zoology, 49(1): 257-265.
  • Eyduran E, Akkus O, Kara MK, Tırınk C, Tarıq M M 2017b. Use of Multivariate Adaptive Regression Splines (Mars) in Predicting Body Weight from Body Measurements in Mengali Rams. International Conference on Agriculture, Forest, Food, Sciences and Technologies (ICAFOF), 11-17 May 2017, Nevşehir, Turkey.
  • Eyduran E, Tirink C, Karahan AE, Türkoğlu M 2017c. Prediction of an upper bound of gene-ralized cross validation in multivariate adaptive regression splines in agricultural studies. International Conference on Computational ans Statistical Methods in Applied Sciences, 9-11 Nov 2017, Samsun Turkey.
  • Eyduran E, Tirink C, Karahan AE, Türkoğlu M, Tariq MM 2017d. Comparison of Predictive Performances of MARS and CART Algorithms through R Software. International Conference on Computational ans Statistical Methods in Applied Sciences, 9-11 Nov 2017, Samsun, Turkey.
  • Eyduran E, Akin M, Eyduran SP 2019a. Application of Multivariate Adaptive Regression Splines in Agricultural Sciences through R Software. Nobel Bilimsel Eserler.
  • Eyduran E 2019b. ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.0. https://CRAN.R-project.org/package=ehaGoF
  • Friedman JH 1991. Multivariate Adaptive Regression Splines. Annls. Stat. 19:1-141.
  • Hastie T, Tibshirani R, Friedman J 2009. The Elements of Statistical Learning: Data Mining, Inference and Prediction, second ed. Springer.
  • Kibet CE 2012. A Multıvarıate Adaptıve Regressıon Splınes Approach to Predıct the Treatment Outcomes of Tuberculosıs Patıents in Kenya. Science in Biometry to The University of Nairobi, Yüksek Lisans Tezi,70s.
  • Ko M, Clark JG, Ko D 2008. Revisiting the Impact of Information Technology Investments on Productivity: An empirical investigation using multivariate adaptive regression splines (MARS). Information Resources Management Journal, 21(3):1-23.
  • Koyun M, Celik S. 2020. Investigation on Some Ectoparasites of Mesopotamian Spiny Eels (Mastacembelus mastacembelus) with Certain Data Mining Algorithms Based on the Effect of Weight and Sex. Pakistan Journal of Zoology, 52(2): 733.
  • Milborrow S 2018. Milborrow. Derived from mda:mars by T. Hastie and R. Tibshirani. url: https://CRAN.R-project.org/package=earth (Erişim tarihi: 31.08. 2020).
  • Oguntunji AO 2017. Regression Tree Analysis for Predicting Body Weight of Nigerian Muscovy Duck (Cairina moschata). Genetika, 49(2): 743-753, 2017.
  • Orhan H, Teke Ç E, Karcı Z 2018. Laktasyon Eğrileri Modellemesinde Çok Değişkenli Uyar-lanabilir Regresyon Eğrileri (Mars) Yönteminin Uygulanması. KSU J Agric Nat 21(3): 363-373.
  • Put R, Xu QS, Massart DL, Vander Heyden 2004. Multivariate adaptive regressionsplines (MARS) in chromatographic quantitative structure–retention relationship studies. Journal of Chromatography A, 1055 : 11-19.
  • R Core Team 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, http://www.R-project.org.
  • Sahin G, Eyduran E, Turkoglu M, Sahin F 2018. Estimation of Global Irradiation Parameters at Location of Migratory Birds in Igdir, Turkey by Means of MARS Algorithm. Pakistan Journal of Zoology, 50(6): 2317-2324.
  • Sengul T, Celik S, And Sengul AY 2018. Bıldırcınlarda Göğüs Etinin Rengi ve Ph’sı Üzerine Yaş, Cinsiyet ve Canlı Ağırlığın Etkisi. Türk Tarım ve Doğa Bilimleri Dergisi, 5(4): 523-529.
  • Sengul AY, Sengul T, Celik S 2020. Relationships Between Body Weight and Some Egg Quality Traits in Japanese Quails. Turkish Journal of Agriculture-Food Science and Technology, 8(2): 308-312.
  • Sevgenler H 2019. Keçilere Ait Kimi Özelliklerin Canlı Ağırlık Üzerindeki Etkilerini Belirlemek Amacıyla Kullanılan Veri Madenciliği Algoritmalarının (Cart, Chaıd Ve Mars) Karşılaştırılması. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Bahçe Bitkileri Anabilim Dalı, Yüksek Lisans Tezi, 57s.
  • Sevimli Y 2009. Çok Değişkenli Uyarlanabilir Regresyon Uzanımlarının Bir Split Mouth Ça-lışmasında Uygulaması. Marmara Üniversitesi, Sağlık Bilimleri Enstitüsü Biyoistatistik Anabilim Dalı, Yüksek Lisans Tezi, 87 s.
  • Steinberg D 2001. An alternative to neural networks: Multivariate adaptive regression splines (MARS), PC AI, January/February, pp. 38 -41.
  • Yakubu A 2012. Application of regression tree methodology in predicting the body weight of Uda sheep. Anim. Sci. Biotechnol., 45: 484-490.
  • Yerlikaya FA 2008. New Contribution to Nonlinear Robust Regression and Classification with Mars and Its Applications to Data Mining for Quality Control in Manufacturing, Master Thesis, METU, Ankara.
  • Xu QS, Daeyaert F, Lewi PJ, Massart DL 2006. Studies of relationship between biological activities and HIV Reverse Transcriptase Inhibitors by Multivariate Adaptive Regression Splines with Curds and Whey. Chemometrics and Intelligent Laboratory Systems, 82: 24-30.
  • Zhang W, Goh AT 2016. Multivariate Adaptive Regression Splines And Neural Network Models For Prediction Of Pile Drivability. Geoscience Frontiers, 7(1): 45-52.

R kullanarak Mars Veri Madenciliği Algoritması ile Yumurta Ağırlığı Tahmini

Year 2021, Volume: 24 Issue: 1, 242 - 251, 28.02.2021

Demet Çanga , Esra Yavuz , Ercan Efe

https://doi.org/10.18016/ksutarimdoga.vi.716880
Cited By: 3

Abstract

Kanatlı hayvanlarda, yumurta ağırlığını belirlemede yumurtanın iç ve dış kalite özellikleri oldukça önemlidir. Yumurtanın kalite özellikleri, gerek kuluçka üretimi ve gerekse yemeklik yumurta üretimi açısından büyük önem taşımaktadır. Bu çalışmanın amacı, Lohmann LSL Classic beyaz hibrit sürü yumurtaları kullanılarak yumurtanın ic dış kalite özellikleri ile yumurta ağırlığının tahminini MARS (Multivariate Adaptive Regression Splines) yöntemi ile modellemektir. Bu amacı gerçekleştirmek için Lohmann LSL Classic beyaz hibrit sürü (n = 60) yumurtaları kullanıldı. Haftalık yumurta verimleri 22. haftadan 62. haftaya kadar değerlendirilmiştir. Bağımlı ve sürekli değişken olarak belirlenen yumurta ağırlığını (EW) tahmin etmek için; şekil indeksi (SI), kabuk kırılma mukavemeti (SBS), kabuk ağırlığı (SW), kabuk kalınlığı (ST), yumurta sarısı çapı (YD), yumurta sarısı genişliği (YW), yumurta sarısı yüksekliği (YH), yumurta sarısı rengi (YC) albümin genişliği (AW), albümin uzunluğu (AL), albümin yüksekliği (AH) kullanılmıştır. Mükemmel uyum iyiliği elde etmek için, R programının “earth” paketinde, penalty = -1, derece = 2, nprune = 10 ve nk = 60 tanımları yapıldı. Araştırma sonucunda mars tahmin modeli, EW = 63.1-0.906 * max (0,75-SI) -0.321 * max (0, SI-75)-62.4*max(0,0.57-ST)-354*max(0,ST 0.57)+1.13*Groupa2*max (0,75-SI)+1.49* max(0.0.57-ST) * YD + 8.2 * max(0, ST-0.57)*YD-0.02*max(0 YD-38.5)*YC-0.0366* YH*max(0,13-YC) olarak belirlendi. Sonuç olarak, bazı kalite değişkenlerinin yumurta ağırlığının belirlenmesinde önemli olduğu bulunmuştur.Bağımlı değişken olarak belirlenen yumurtanın ağırlığını tahmin ederken a2, SI, YC, ST, YD, YH görülürken, diğer değişkenler bu denkleme dahil edilmemiştir. Tavukçulukta, MARS tahmin modeli, daha kolay formül ve daha yüksek doğruluk ile yumurta ağırlığını tahmin etmede klasik lineer olmayan modellere daha iyi bir alternatif olabilir.

Keywords

MARS Yumurta ağırlığı Lohman ırkı Uyum iyiliği Parametrik olmayan regresyon modeli

References

  • Akin M, Eyduran, SP, Eyduran E 2020. Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. Plant Cell Tiss Organ Cult 140, 661–670.
  • Aksoy A, Erturk YE, Eyduran E and Tariq MM 2018a. Comparing predictive performances of MARS and CHAID algorithms for defining factors affecting final fattening live weight in cultural beef cattle enterprises. Pakistan Journal of Zoology, 50(6): 2279-2286.
  • Aksoy A, Erturk YE, Eyduran E, Tariq MM 2018b. Utility of MARS Algorithm for Describing Non-Genetic Factors Affecting Pasture Revenue of Morkaraman Breed And Romanov × Morkaraman F1 Crossbred Sheep Under Semi İntensive Conditions. Pakistan Journal of Zoology, 51(1):235-240.
  • Aytekin I, Eyduran E, Karadas K, Akşahan R, Keskin I 2018. Prediction of Fattening Final Live Weight from Some Body Measurements and Fattening Period in Young Bulls Of Crossbred And Exotic Breeds Using Mars Data Mining Algorithm. Pakistan Journal of Zoology, 50(1):189-195.
  • Banks DL, Olszewski RT, Maxion RA 2003. Comparing Methods for Multivariate Adaptive Regression. Communication in Statistics-Simulation and Computation, 32(2):541-571. [Electronic Journal], http://www.informaworld.com/smpp/title~content=t713597237.
  • Banks DL. 2001. Exploratory Data Analysis: Multivariate Approaches (Nonparametric Reg-ression). In: International Encyclopedia of the Social & Behavioral Sciences. Eds: Smelser NJ, Baltes PB. Vol 8, 2nd ed, Elsevier, Amsterdam, p 5164-5169.
  • Banks DL. 2001. Exploratory Data Analysis: Multivariate Approaches (Nonparametric Reg-ression). In: International Encyclopedia of the Social & Behavioral Sciences. Eds: Smelser NJ, Baltes PB. Vol 8, 2nd ed, Elsevier, Amsterdam, p 5164-5169.
  • Canga D, Boga M 2019. Hayvancılıkta Mars Kullanımı Ve Bır Uygulama. III. International Scientific and Vocational Studies Congress – Science and Health 27-30 June 2019, Ürgüp, Nevşehir / Turkiye.
  • Celik S, Eyduran E, Kardaş K, Tariq MM. 2017. Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan. Revista Brasileira de Zootecnia, 46(11): 863-872.
  • Celik S, Yilmaz O. 2018. Prediction of Body Weight of Turkish Tazi Dogs using Data Mining Techniques: Classification and Regression Tree (CART) and Multivariate Adaptive Reg-ression Splines (MARS). Pakistan Journal of Zoology, 50(2): 575-583 doi:10.17582/ journal.pjz/2018.50.2.575.583
  • Celik S. 2019. Comparing Predictive Performances of Tree-Based Data Mining Algorithms and MARS Algorithm in the Prediction of Live Body Weight from Body Traits in Pakistan Goats. Pakistan Journal of Zoology, 51(4):1447.
  • Celik, S. and Boydak E. 2020. Description of The Relationships Between Different Plant Characteristics in Soybean Using Multivariette Adaptıive Regression Splines (Mars) Algorithm. Japs, Journal of Animal and Plant Sciences, 30(2): 431-441.
  • Deconinck E, Xu QS, Put R, Coomans D, Massart DL, Heyden YV 2005. Prediction ofgastro-intestinal absorption using multivariate adaptive regression splines. Journal of Pharmaceutical and Biomedical Analysis, 39: 1021-1030.
  • Erturk YE, Aksoy A, Tariq MM 2018. Effect of Selected Variables Identified by MARS on Fattening Final Live Weight of Crossbred Beef Cattle in Eastern Turkey. Pakistan Journal of Zoology, 50(4):1403-1412.
  • Eyduran E, Zaborski D, Waheed A, Celik S, Karadas, K, Grzesiak W 2017a. Comparison of the predictive capabilities of several data mining algorithms and multiple linear regression in the prediction of body weight by means of body measurements in the indigenous Beetal goat of Pakistan. Pakistan Journal of Zoology, 49(1): 257-265.
  • Eyduran E, Akkus O, Kara MK, Tırınk C, Tarıq M M 2017b. Use of Multivariate Adaptive Regression Splines (Mars) in Predicting Body Weight from Body Measurements in Mengali Rams. International Conference on Agriculture, Forest, Food, Sciences and Technologies (ICAFOF), 11-17 May 2017, Nevşehir, Turkey.
  • Eyduran E, Tirink C, Karahan AE, Türkoğlu M 2017c. Prediction of an upper bound of gene-ralized cross validation in multivariate adaptive regression splines in agricultural studies. International Conference on Computational ans Statistical Methods in Applied Sciences, 9-11 Nov 2017, Samsun Turkey.
  • Eyduran E, Tirink C, Karahan AE, Türkoğlu M, Tariq MM 2017d. Comparison of Predictive Performances of MARS and CART Algorithms through R Software. International Conference on Computational ans Statistical Methods in Applied Sciences, 9-11 Nov 2017, Samsun, Turkey.
  • Eyduran E, Akin M, Eyduran SP 2019a. Application of Multivariate Adaptive Regression Splines in Agricultural Sciences through R Software. Nobel Bilimsel Eserler.
  • Eyduran E 2019b. ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.0. https://CRAN.R-project.org/package=ehaGoF
  • Friedman JH 1991. Multivariate Adaptive Regression Splines. Annls. Stat. 19:1-141.
  • Hastie T, Tibshirani R, Friedman J 2009. The Elements of Statistical Learning: Data Mining, Inference and Prediction, second ed. Springer.
  • Kibet CE 2012. A Multıvarıate Adaptıve Regressıon Splınes Approach to Predıct the Treatment Outcomes of Tuberculosıs Patıents in Kenya. Science in Biometry to The University of Nairobi, Yüksek Lisans Tezi,70s.
  • Ko M, Clark JG, Ko D 2008. Revisiting the Impact of Information Technology Investments on Productivity: An empirical investigation using multivariate adaptive regression splines (MARS). Information Resources Management Journal, 21(3):1-23.
  • Koyun M, Celik S. 2020. Investigation on Some Ectoparasites of Mesopotamian Spiny Eels (Mastacembelus mastacembelus) with Certain Data Mining Algorithms Based on the Effect of Weight and Sex. Pakistan Journal of Zoology, 52(2): 733.
  • Milborrow S 2018. Milborrow. Derived from mda:mars by T. Hastie and R. Tibshirani. url: https://CRAN.R-project.org/package=earth (Erişim tarihi: 31.08. 2020).
  • Oguntunji AO 2017. Regression Tree Analysis for Predicting Body Weight of Nigerian Muscovy Duck (Cairina moschata). Genetika, 49(2): 743-753, 2017.
  • Orhan H, Teke Ç E, Karcı Z 2018. Laktasyon Eğrileri Modellemesinde Çok Değişkenli Uyar-lanabilir Regresyon Eğrileri (Mars) Yönteminin Uygulanması. KSU J Agric Nat 21(3): 363-373.
  • Put R, Xu QS, Massart DL, Vander Heyden 2004. Multivariate adaptive regressionsplines (MARS) in chromatographic quantitative structure–retention relationship studies. Journal of Chromatography A, 1055 : 11-19.
  • R Core Team 2014. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, http://www.R-project.org.
  • Sahin G, Eyduran E, Turkoglu M, Sahin F 2018. Estimation of Global Irradiation Parameters at Location of Migratory Birds in Igdir, Turkey by Means of MARS Algorithm. Pakistan Journal of Zoology, 50(6): 2317-2324.
  • Sengul T, Celik S, And Sengul AY 2018. Bıldırcınlarda Göğüs Etinin Rengi ve Ph’sı Üzerine Yaş, Cinsiyet ve Canlı Ağırlığın Etkisi. Türk Tarım ve Doğa Bilimleri Dergisi, 5(4): 523-529.
  • Sengul AY, Sengul T, Celik S 2020. Relationships Between Body Weight and Some Egg Quality Traits in Japanese Quails. Turkish Journal of Agriculture-Food Science and Technology, 8(2): 308-312.
  • Sevgenler H 2019. Keçilere Ait Kimi Özelliklerin Canlı Ağırlık Üzerindeki Etkilerini Belirlemek Amacıyla Kullanılan Veri Madenciliği Algoritmalarının (Cart, Chaıd Ve Mars) Karşılaştırılması. Iğdır Üniversitesi Fen Bilimleri Enstitüsü Bahçe Bitkileri Anabilim Dalı, Yüksek Lisans Tezi, 57s.
  • Sevimli Y 2009. Çok Değişkenli Uyarlanabilir Regresyon Uzanımlarının Bir Split Mouth Ça-lışmasında Uygulaması. Marmara Üniversitesi, Sağlık Bilimleri Enstitüsü Biyoistatistik Anabilim Dalı, Yüksek Lisans Tezi, 87 s.
  • Steinberg D 2001. An alternative to neural networks: Multivariate adaptive regression splines (MARS), PC AI, January/February, pp. 38 -41.
  • Yakubu A 2012. Application of regression tree methodology in predicting the body weight of Uda sheep. Anim. Sci. Biotechnol., 45: 484-490.
  • Yerlikaya FA 2008. New Contribution to Nonlinear Robust Regression and Classification with Mars and Its Applications to Data Mining for Quality Control in Manufacturing, Master Thesis, METU, Ankara.
  • Xu QS, Daeyaert F, Lewi PJ, Massart DL 2006. Studies of relationship between biological activities and HIV Reverse Transcriptase Inhibitors by Multivariate Adaptive Regression Splines with Curds and Whey. Chemometrics and Intelligent Laboratory Systems, 82: 24-30.
  • Zhang W, Goh AT 2016. Multivariate Adaptive Regression Splines And Neural Network Models For Prediction Of Pile Drivability. Geoscience Frontiers, 7(1): 45-52.
There are 40 citations in total.

Details

Primary Language English
Subjects Agricultural, Veterinary and Food Sciences
Journal Section RESEARCH ARTICLE
Authors

Demet Çanga 0000-0003-3319-7084

Esra Yavuz 0000-0002-5589-297X

Ercan Efe 0000-0002-5131-323X

Publication Date February 28, 2021
Submission Date April 9, 2020
Acceptance Date June 30, 2020
Published in Issue Year 2021Volume: 24 Issue: 1

Cite

APA Çanga, D., Yavuz, E., & Efe, E. (2021). Prediction of Egg Weight Using MARS data mining Algorithm through R. Kahramanmaraş Sütçü İmam Üniversitesi Tarım Ve Doğa Dergisi, 24(1), 242-251. https://doi.org/10.18016/ksutarimdoga.vi.716880

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