Using a decision tree classifier from a machine learning library is often awkward because it usually must be customized and library decision trees have many complex supporting functions, says resident ...

The two main downsides to decision trees are that they often don't work well with large datasets, and they are highly susceptible to model overfitting. When tackling a binary classification problem, ...

Malware incidents cost organizations and industries billions of dollars every year. In a 2012 worldwide survey on the financial impacts of malware, more than 2,600 business leaders and IT security ...

Deciding what to do in situations when there is no right answer is an unenviable challenge for any leader. Doing it in a matter of seconds under the pressure of a rapidly changing scenario — like a ...

Computer scientists often encounter problems relevant to real-life scenarios. For instance, "multiagent problems," a category characterized by multi-stage decision-making by multiple decision makers ...

For better accountability, we should shift the focus from the design of these systems to their impact. Describing a decision-making system as an “algorithm” is often a way to deflect accountability ...

An innovative algorithm called Spectral Expansion Tree Search helps autonomous robotic systems make optimal choices on the move. In 2018, Google DeepMind's AlphaZero program taught itself the games of ...