A review of predictive uncertainty estimation with machine learning

Abstract

Predictions and forecasts of machine learning models should take the form of probability distributions, aiming to increase the quantity of information communicated to end users. Although applications of probabilistic prediction and forecasting with machine learning models in academia and industry are becoming more frequent, related concepts and methods have not been formalized and structured under a holistic view of the entire field. Here, we review the topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions. The review covers a time period spanning from the introduction of early statistical (linear regression and time series models, based on Bayesian statistics or quantile regression) to recent machine learning algorithms (including generalized additive models for location, scale and shape, random forests, boosting and deep learning algorithms) that are more flexible by nature. The review of the progress in the field, expedites our understanding on how to develop new algorithms tailored to users’ needs, since the latest advancements are based on some fundamental concepts applied to more complex algorithms. We conclude by classifying the material and discussing challenges that are becoming a hot topic of research.

Predictive Modelling on Competitor Analysis Performance by using Generalised Linear Models and Machine Learning Approach

“…Based on the professional statistician viewpoint regarding market modelling field, there are few types of statistical models that are appropriate [12] to use in the market research which will be discussed in the next section. Other than traditional mathematical modelling, machine learning is one of the frequently used recently when dealing with uncertainty and big data [8], [15], [19], [20], [4], [5]. Barboza, F., Kimura, H., & Altman, E. [5] use machine learning models to predict bankruptcy and found that machine learning models show improved bankruptcy prediction accuracy over traditional models [12].…”