Adopting Some Good Practices to Avoid Overfitting in the Use of Machine Learning

Authors: , ,

Abstract: In Machine Learning, different techniques, methods and algorithms are applied in order to a better approach for the problem that is solving. Adaptive learning, self-organization of information, generalization, fault tolerance and real-time operation are some of the most used in this field. These systems are dynamic and they can learn from the data adapting to the nature of the information. But an excessive adaptation or improvement of the response to the training data can lead to a poor generalization in many cases. Excessive training with the same set of data will cause the classification curves to over-detail the formal variations of that set. To avoid this overfitting, certain preventions can be taken. One possible option is to use the regularization technique keeping all the variables. This technique works well when we have many input parameters and each contributes a little in the prediction. We can conclude that the number of input features compared with the number of training samples, is really important to avoid overfitting.

WSEAS Transactions on Mathematics, ISSN / E-ISSN: 1109-2769 / 2224-2880, Volume 17, 2018, Art. #34

PDF
XML
Cite
×
Citation Tools
WSEAS AMA
Imanol Bilbao, Javier Bilbao, Cristina Feniser, "Adopting Some Good Practices to Avoid Overfitting in the Use of Machine Learning," WSEAS Transactions on Mathematics, vol. 17, pp. 274-279, 2018, DOI:
Imanol Bilbao, Javier Bilbao, Cristina Feniser. Adopting Some Good Practices to Avoid Overfitting in the Use of Machine Learning. WSEAS Transactions on Mathematics. 2018;17:274-279.
Copy to Clipboard
Citation copied to Clipboard

Certification

---