Análisis comparativo entre la ecuación de gamma incompleta y técnicas de aprendizaje automático

This thesis aims to perform the comparative analysis between the incomplete gamma equation and machine learning techniques for the adjustment of dairy production in cattle of the Holstein Friesian breed from Ecuador. For this development was implemented the technique of systematic review of the literature for the search and selection of information, the agile development methodology (XP) Extreme Programming to develop the code and implement the incomplete gamma equation; for the development of data mining, modeling, and evaluation of investigated algorithms was used the CRISP-DM methodology, and the comparative method for the analysis of the indices obtained between the incomplete gamma equation and the machine learning algorithms. As a result, the machine learning model selected, in this case, the artificial neural network obtained a better R2 of 0.94 correlated with the information criterion of Akaike or AIC.

• Κύριος συγγραφέας: Loor Morales, Manuel Enrique (author)
• Άλλοι συγγραφείς: Zambrano Zambrano, Jofre Agustín (author)
• Μορφή: bachelorThesis
• Έκδοση: 2026
• Θέματα: Gamma incompleta; Aprendizaje automático; Precisión; IA para producción láctea
• Διαθέσιμο Online: https://repositorio.espam.edu.ec/handle/42000/2897