Error metrics and performance fitness indicators for artificial intelligence and machine learning in engineering and sciences
Artificial intelligence (AI) and Machine learning (ML) train machines to achieve a high level
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
of cognition and perform human-like analysis. Both AI and ML seemingly fit into our daily …
[HTML][HTML] Machine learning methods for wind turbine condition monitoring: A review
This paper reviews the recent literature on machine learning (ML) models that have been
used for condition monitoring in wind turbines (eg blade fault detection or generator …
used for condition monitoring in wind turbines (eg blade fault detection or generator …
[HTML][HTML] The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
Regression analysis makes up a large part of supervised machine learning, and consists of
the prediction of a continuous independent target from a set of other predictor variables. The …
the prediction of a continuous independent target from a set of other predictor variables. The …
[HTML][HTML] The M4 Competition: 100,000 time series and 61 forecasting methods
The M4 Competition follows on from the three previous M competitions, the purpose of which
was to learn from empirical evidence both how to improve the forecasting accuracy and how …
was to learn from empirical evidence both how to improve the forecasting accuracy and how …
A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods
Floods around the world are having devastating effects on human life and property. In this
paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS …
paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS …
[HTML][HTML] State-of-the-art review of soft computing applications in underground excavations
Soft computing techniques are becoming even more popular and particularly amenable to
model the complex behaviors of most geotechnical engineering systems since they have …
model the complex behaviors of most geotechnical engineering systems since they have …
Performance metrics (error measures) in machine learning regression, forecasting and prognostics: Properties and typology
A Botchkarev - arXiv preprint arXiv:1809.03006, 2018 - arxiv.org
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 …
various fields. The intention of this study was to overview of a variety of performance metrics …
Factors affecting carbon emissions in emerging economies in the context of a green recovery: Implications for sustainable development goals
As governments, business leaders, and other stakeholders discuss green recovery, this
research examines the combined effects of energy poverty, renewable energy consumption …
research examines the combined effects of energy poverty, renewable energy consumption …
Mean absolute percentage error for regression models
We study in this paper the consequences of using the Mean Absolute Percentage Error
(MAPE) as a measure of quality for regression models. We prove the existence of an optimal …
(MAPE) as a measure of quality for regression models. We prove the existence of an optimal …
[HTML][HTML] A new metric of absolute percentage error for intermittent demand forecasts
S Kim, H Kim - International Journal of Forecasting, 2016 - Elsevier
The mean absolute percentage error (MAPE) is one of the most widely used measures of
forecast accuracy, due to its advantages of scale-independency and interpretability …
forecast accuracy, due to its advantages of scale-independency and interpretability …