Each technique has pros and cons. This article explains how to implement decision tree regression from scratch, using the C# language. Compared to other regression techniques, decision tree regression ...

The random forest model produces an ensemble of randomized decision trees, and is used for both classification and regression. The aggregated ensemble either combines the votes modally or averages ...

This month we'll look at classification and regression trees (CART), a simple but powerful approach to prediction 3. Unlike logistic and linear regression, CART does not develop a prediction equation.

A new AI algorithm can associate brain activity with certain behaviors, an advancement that can help improve brain-computer interfaces and find new patterns in neural activity.

After developing and implementing AI-powered applications across multiple industries, I firmly believe that AI is not a novelty—it is redefining how businesses operate.

This case study evaluates modelling relationships between the combination of decision variables and uncertain factors. There are 6 uncertain factors that influence water quality varying within a ...

New research can transform how hospitals triage, risk-stratify, and counsel patients to save lives.

Non-AI-based digital tree models are quite complicated, involving simulation algorithms that consider many mutually affecting nonlinear factors. Such models are needed in endeavors such as ...

Decision tree regression is a fundamental technique that can be used by itself, and is also the basis for powerful ensemble techniques (a collection of many decision trees), notably, AdaBoost ...