The predictive accuracy of 5 machine learning classifiers (logistic regression classifier, random forest classifier, support vector machine, k-nearest neighbor, and adaptive boosting) was examined ...

This study published in Robot Learning has been focused on water analysis using the combination of decision making and machine learning for a recently developed robotic system. The unique procedure ...

Creating an open source classifier for bats is also potentially useful for the world outside of Machine Learning as it could not only enable us to more easily monitor bats themselves, but also the ...

Cédric Beaulac, Jeffrey S. Rosenthal, Predicting University Students' Academic Success and Major Using Random Forests, Research in Higher Education, Vol. 60, No. 7 (NOVEMBER 2019), pp. 1048-1064 ...

Overview Understanding key machine learning algorithms is crucial for solving real-world data problems effectively.Data ...

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other ...