# Supervised Machine Learning Techniques: An Overview with Applications to Banking

arXiv: https://arxiv.org/abs/2008.04059 · HTML: https://mlsanity.com/paper/2008.04059/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [Quantifying Inherent Randomness in Machine Learning Algorithms](https://mlsanity.com/paper/2206.12353/) (2022)
- [Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study](https://mlsanity.com/paper/2204.12868/) (2022)
- [Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights](https://mlsanity.com/paper/2208.06096/) (2022)
- [On marginal feature attributions of tree-based models](https://mlsanity.com/paper/2302.08434/) (2023)
- [Assessing Robustness of Machine Learning Models using Covariate Perturbations](https://mlsanity.com/paper/2408.01300/) (2024)
- [Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation](https://mlsanity.com/paper/2211.08536/) (2022)
- [Cross Spline Net and a Unified World](https://mlsanity.com/paper/2410.19154/) (2024)

## Abstract (arXiv)

This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural Networks (NNs). We begin with an introduction to ML tasks and techniques. This is followed by a description of: i) tree-based ensemble algorithms including Bagging with RF and Boosting with GBMs, ii) Feedforward NNs, iii) a discussion of hyper-parameter optimization techniques, and iv) machine learning interpretability. The paper concludes with a comparison of the features of different ML algorithms. Examples taken from credit risk modeling in banking are used throughout the paper to illustrate the techniques and interpret the results of the algorithms.