Mean Absolute Percentage Error for regression models
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TLDR
It is proved the existence of an optimal MAPE model and the universal consistency of Empirical Risk Minimization based on the MAPE is shown, and it is shown that finding the best model under theMAPE is equivalent to doing weighted Mean Absolute Error regression, and this weighting strategy is applied to kernel regression.About:
This article is published in Neurocomputing.The article was published on 2016-06-05 and is currently open access. It has received 619 citations till now. The article focuses on the topics: Symmetric mean absolute percentage error & Mean absolute percentage error.read more
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CatBoost for big data: an interdisciplinary review
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A review of the-state-of-the-art in data-driven approaches for building energy prediction
TL;DR: This paper provides a comprehensive review on building energy prediction, covering the entire data-driven process that includes feature engineering, potential data- driven models and expected outputs, and concludes with some potential future research directions based on discussion of existing research gaps.
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Extraction of mechanical properties of materials through deep learning from instrumented indentation
TL;DR: A multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy and accuracy is utilized.
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Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error
TL;DR: In this paper, the use of the Mean Absolute Deviation and Mean Absolute Percentage Error to calculate the percentage of mistakes in the least square method resulted in a percentage of 9.77%.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Journal ArticleDOI
Robust Estimation of a Location Parameter
TL;DR: In this article, a new approach toward a theory of robust estimation is presented, which treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators that are asyptotically most robust (in a sense to be specified) among all translation invariant estimators.
Book
A Distribution-Free Theory of Nonparametric Regression
TL;DR: How to Construct Nonparametric Regression Estimates * Lower Bounds * Partitioning Estimates * Kernel Estimates * k-NN Estimates * Splitting the Sample * Cross Validation * Uniform Laws of Large Numbers
Book
Neural Network Learning: Theoretical Foundations
Martin Anthony,Peter L. Bartlett +1 more
TL;DR: The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction, and discuss the computational complexity of neural network learning.
Journal ArticleDOI
Error measures for generalizing about forecasting methods: Empirical comparisons
J. Scott Armstrong,Fred Collopy +1 more
TL;DR: In this article, the authors evaluated measures for making comparisons of errors across time series and found that the median absolute error of a given method to that from the random walk forecast is not reliable, and therefore inappropriate for comparing accuracy across series.