Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study. (arXiv:2204.12868v1 [stat.ML])

This paper compares the performances of three supervised machine learning
algorithms in terms of predictive ability and model interpretation on
structured or tabular data. The algorithms considered were scikit-learn
implementations of extreme gradient boosting machines (XGB) and random forests
(RFs), and feedforward neural networks (FFNNs) from TensorFlow. The paper is
organized in a findings-based manner, with each section providing general
conclusions supported by empirical results from simulation studies that cover a
wide range of model complexity and correlation structures among …

algorithms arxiv interpretability learning machine machine learning machine learning algorithms ml performance study supervised machine learning