A comparison of machine learning algorithms in predicting nonnormal continuous outcome variables

Abstract

Machine learning is a type of data analysis that creates prediction models by learning from a portion of the data set. These algorithms can be used in many disciplines to answer complex questions and hypotheses. There are many available algorithms, each with their own strengths and weaknesses. Much research has been compiled on each algorithm individually to show where they excel and provide context into many use cases. The purpose of this research project is to document a comparison of BART, Random Forest, and GBM; A few top machine learning algorithms on their ability to predict nonnormal continuous outcome variables. The results of this study could help determine which prediction models preform the most efficiently and accurately when building predictive models for nonnormal continuous outcome variables.