It is possible to post-process decision tree prediction rules to remove unnecessary duplication of predictor variables, but the demo program, and most machine learning library implementations, do not ...

In machine learning, typically non-linear regression techniques are used. Examples of nonlinear regression algorithms include gradient descent, Gauss-Newton, and the Levenberg-Marquardt methods.

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 ...

"Clinical decision support systems, for example, are designed to help practitioners stay up to date on new developments without requiring them to spend their entire day reading the medical literature.

Compared to other regression techniques, decision tree regression is easy to tune, works well with small datasets and produces highly interpretable predictions. However, decision tree regression is ...