There is indeed a vast literature on the design and analysis of decision tree algorithms that aim at optimizing these parameters. This paper contributes to this important line of research: we propose ...

A review of the military’s Family Advocacy Program shows that Incident Determination Committees make administrative abuse ...

Many crypto industry professional struggle to keep bank accounts. Here are the reasons behind mass debanking in the U.S., ...

Discover the power of predictive modeling to forecast future outcomes using regression, neural networks, and more for improved business strategies and risk management.

Computer vision systems combined with machine learning techniques have demonstrated success as alternatives to empirical methods for classification and selection. This study aimed to classify tomatoes ...

Abstract: This paper presents an automatic machine learning (autoML) algorithm to select a decision tree algorithm which is most suitable for the stated requirements by the user for classification.

Abstract: Decision tree algorithms are very useful approaches in data mining. Indeed, the C4.5 algorithm is a popular data classifier for machine learning. Nowadays there is a wide range of Big Data ...

Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, ...

ABSTRACT: Land use and occupation dynamics impact landscape structure, diversity, richness and balance of vegetation cover. The aim of this study is to describe the process of fragmentation of the ...

This week I interviewed Senator Amy Klobuchar, Democrat of Minnesota, about her Preventing Algorithmic Collusion Act. If you don’t know what algorithmic collusion is, it’s time to get educated, ...