Abstract
Within the area of data science, the hyper parameters arguments affect the execution of the algorithms and due to their particularity, must be used separately for each machine learning model in quantitative predictions, known as regression. Two quality metrics from regression models will be used in order to demonstrate the changes: Mean Square Error, (MSE) and the value of the R2 coefficient.
In the present work, using simulation of the interception coefficient, it will be demonstrated the existence of machine learning algorithms that are sensitive: Mean Square Error and/or the value of the R2 coefficient.
It is important to highlight that the intercept coefficient is considered as a reference argument. The present research is only a very small space of an automated machine learning process (AutoML) with regard to sensitivity analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hutter, F., Kotthoff, L., Vanschoren, J.: Automated Machine Learning Methods, Systems, Challenges. Springer, Barcelona (2019)
Long, J., Teetor, P., Cookbook, R.: Proven Recipes for Data Analysis, Statistics & Graphics, 2nd edn. O’Reilly Media Inc, Sebastopool (2019)
Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to Linear Regression Analysis, 3rd edn. Continental Publishing Company, New York (2012)
Grus, J.: Data science from scratch. In: Cronin, M. (ed.) First Principles with Python, 2nd edn. O’Reilly Media Inc, Sebastopol (2019)
Orellana Alvear, J.: Arboles de decisión y Random Forest, Universidad de Cuenca, 12 November 2018. https://bookdown.org/content/2031/. Accessed 1 Mar 2021
Sullivan, W.: Machine Learning For Beginners: Algorithms, Decision Tree & Random Forest Introduction. Healthy Pragmatic Solutions, New York (2017)
Suykens, J.A.: Regularization, Optimization, Kernels, and Support Vector Machines. CRC Press, New York (2014)
Hernández Orallo, J., Ferri Ramírez, C., Ramírez Quintana, M.J.: Introducción a la minería de datos. Pearson Educación, Madrid (2004)
Duboue, P.: The Art of Feature Engineering: Essentials for Machine Learning, pp. 20–25. Cambridge University Press, New York (2020)
RiveraJorda, E., Raqueno, A.: Predictive model for the academic performance of the engineering students using CHAID and C 5.0 algorithm. Int. J. Eng. Res. Technol. 12(6), 917–918 (2019)
Meghana, S., Trupti, P.: Consolidation of C 5.0, random forest and random tree crafts for intrusion detection system. Int. J. Res. Eng. Sci. Manage. 2(11), 432–435 (2019)
Tatsat, H., Puri, S., Lookabaugh, B.: Machine Learning and Data Science Blueprint for Finance. Oreilly, Sebatopol (2021)
Myers, R.H., Myers, S.L., Walpole, R.E.: Probability and Statistics For Engineers - 6b. Prentice Hall, New York (2000)
Goic, M.: Duality and Sensitivity Analysis, 1 January 2020. https://www.u-cursos.cl/usuario/e4ec9e12c4e47e3de09b0ff5dbe14eb0/mi_blog/r/dualidad.pdf. Accessed 1 Mar 2021
Muñoz-Cañón, N.D., Romero-Triana, J.A.: Optimization of the hyperparameters of a support vector regression machine using particle swarm for forecasting COVID-19 cases. Revista UIS Ingenierías 20(2), 181–196 (2021)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufman, New York (2005)
Usuelli, M.: Essentials, R Machine Learning. Packt, New York (2012)
Zhao, Y.: R and Data Mining Examples and Case Studies. Academic Press, New York (2012)
Fisher, R.: Iris Data Set (1988). https://archive.ics.uci.edu/ml/datasets/iris
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Echeverria, F., Leon, M., Esteves, Z., Redroban, C. (2022). Variation of the Intercession Coefficient Used as a Hyper Parameter in Machine Learning in Regression Models. In: Misra, S., Oluranti, J., Damaševičius, R., Maskeliunas, R. (eds) Informatics and Intelligent Applications. ICIIA 2021. Communications in Computer and Information Science, vol 1547. Springer, Cham. https://doi.org/10.1007/978-3-030-95630-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-95630-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95629-5
Online ISBN: 978-3-030-95630-1
eBook Packages: Computer ScienceComputer Science (R0)