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What is Mean Squared Error

Chapter 160
Encyclopedia of Data Science and Machine Learning
Error measure for assessing the quality of a prediction (regression) which is determined as the mean value of the squared deviations between the predicted and actual values.
Published in Chapter:
Predicting Estimated Arrival Times in Logistics Using Machine Learning
Peter Poschmann (Technische Universität Berlin, Germany), Manuel Weinke (Technische Universität Berlin, Germany), and Frank Straube (Technische Universität Berlin, Germany)
Copyright: © 2023 |Pages: 19
DOI: 10.4018/978-1-7998-9220-5.ch160
Abstract
The aim of the article is to demonstrate the use and benefit of machine learning (ML) in logistics by means of a significant, practice-relevant application: the prediction of estimated times of arrival (ETA) in intermodal transport chains. Based on a real use case, the article first provides an approach for the methodical procedure for the implementation of ETA predictions and a description of essential development phases. Subsequently, a cross-actor prediction approach for the combined road-rail transport of containers in the port hinterland is designed, and ML-based prediction models for specific logistics processes are prototypically implemented and evaluated. Finally, an outlook on future research directions is given.
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Chapter 23
Prognostication of Crime Using Bagging Regression Model: A Case Study of London
How closely a regression line resembles the Mean Squared Error determines a set of data points. A risk function corresponds to the squared error loss’s expected value. The average, more particularly the mean, of errors squared from data related to a function is used to determine to mean square error.
Published in Chapter: Prognostication of Crime Using Bagging Regression Model: A Case Study of London; From: Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
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Chapter 16
Detection Approaches for Categorization of Spam and Legitimate E-Mail
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Chapter 28
Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process
Is a measure of the accuracy of the predicted results ( Sood and Jain, 2017 ). If the MSE is zero, it means there is no error ( Al-Shayea, 2011 ).
Published in Chapter: Forecasting Hotel Occupancy Rates With Artificial Neural Networks in the COVID-19 Process; From: Handbook of Research on the Impacts and Implications of COVID-19 on the Tourism Industry
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