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 ...

Overview Understanding key machine learning algorithms is crucial for solving real-world data problems effectively.Data scientists should master both supervised ...

Prognostic models for 12-months mortality were built using Cox regression model, single decision tree (DT) and random survival forest (RSF). Models performance was compared based on externally ...

Recent scientific article explores the use of machine learning techniques to identify the key risk factors associated with ...

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.

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 ...