Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology

by   Alexei Botchkarev, et al.

Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. The intention of this study was to overview of a variety of performance metrics and approaches to their classification. The main goal of the study was to develop a typology that will help to improve our knowledge and understanding of metrics and facilitate their selection in machine learning regression, forecasting and prognostics. Based on the analysis of the structure of numerous performance metrics, we propose a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set.


page 1

page 2

page 3

page 4


Evaluation of Local Explanation Methods for Multivariate Time Series Forecasting

Being able to interpret a machine learning model is a crucial task in ma...

Metrics for Graph Comparison: A Practitioner's Guide

Comparison of graph structure is a ubiquitous task in data analysis and ...

A Look at the Evaluation Setup of the M5 Forecasting Competition

Forecast evaluation plays a key role in how empirical evidence shapes th...

Common Metrics to Benchmark Human-Machine Teams (HMT): A Review

A significant amount of work is invested in human-machine teaming (HMT) ...

Asymmetric Metrics on the Full Grassmannian of Subspaces of Different Dimensions

Metrics on Grassmannians have a wide array of applications: machine lear...

Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties

Knowing chemical soil properties might be determinant in crop management...

Please sign up or login with your details

Forgot password? Click here to reset