TY  - JOUR
T1  - A Random Forest Classifier for Digital Newspaper Readers
AU - Mendoza-Mendoza, Adel AU - La Hoz-Domĺnguez, Enrique De AU - Mendoza-Casseres, Daniel 
JO  - Journal of Engineering and Applied Sciences
VL  - 15
IS  - 22
SP  - 3668
EP  - 3673
PY  - 2020
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2020.3668.3673
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2020.3668.3673
KW  - Random forest
KW  -classification
KW  -newspapers
KW  -supervised learning
KW  -recommender systems
AB  - In this research, the potential of machine
learning methods based on Decision Trees (DT) and
Random Forest (RF) models is developed in the context
of classifying readers of a digital newspaper. For this
purpose, the number of visits of users to each section of
the newspaper in a 6-month interval has been taken into
account. The models of DT and RF developed in this
study, classify the profiles of readers who access the
journal with an accuracy of 98.07% and AUC value of
99.27%, thus, demonstrating that it serves as a valid tool
for making strategic and operational decisions when
creating, manage and present content in the user-website
interaction.
ER  -