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What is Mean Absolute Error

Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
Regression models use the model evaluation statistic known as Mean Absolute Error (MAE). The mean of each prediction error’s absolute value overall test set occurrences is the mean fundamental error of a model concerning that test set. For instance, the difference between the actual value and the expected value is each prediction error.
Published in Chapter:
Prognostication of Crime Using Bagging Regression Model: A Case Study of London
Ashansa Kithmini Wijeratne (Sabaragamuwa University of Sri Lanka, Sri Lanka), Nirubikaa Ravikumar (Sabaragamuwa University of Sri Lanka, Sri Lanka), Pulasthi Mithila Bandara (Sabaragamuwa University of Sri Lanka, Sri Lanka), and Banujan Kuhaneswaran (Sabaragamuwa University of Sri Lanka, Sri Lanka)
DOI: 10.4018/978-1-6684-4755-0.ch023
Crime is a social and economic problem that affects a country's quality of day-to-day life and economic growth. However, analyzing and forecasting crime is not a straightforward job for a law enforcement investigator to manually unravel the underlying nuances of crime data. To make this process easier and more automated, the authors present a machine-learning model for crime analysis and predictions. The authors used a London crime dataset and enhanced the data set by incorporating population density, percentage of economically inactive working age, and average monthly temperature. The pre-process step prepares the raw data and makes it suitable for the machine-learning model. Bagging and boosting ensemble techniques were used to find a better- machine-learning model. GridSearchCV was used to tune hyperparameters to find the best-performed model. Parameters were tuned as an iterative processes. Eventually, the researchers compared all the algorithms and selected the Random Forest bagging regression model as the best-performed algorithm.
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Machine Learning Models for Forecasting of Individual Stocks Price Patterns
The average of differences between absolute values of actual stock price and predicted price of stocks.
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Modelling and Forecasting Portfolio Inflows: A Comparative Study of Support Vector Regression, Artificial Neural Networks, and Structural VAR Models
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Detection Approaches for Categorization of Spam and Legitimate E-Mail
The mean absolute error (MAE) is a quantity used to measure how close predictions are to the eventual outcomes.
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