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What is Evaluation Metrics

Protecting User Privacy in Web Search Utilization
Evaluation metrics are quantitative measures that are used to assess the performance of a model or algorithm. They are used to determine how well the model is performing in terms of accuracy, precision, recall, F1 score, and other measures. The choice of evaluation metrics depends on the type of problem being solved and the objectives of the model.
Published in Chapter:
Web Search Privacy Evaluation Metrics
Rafi Ullah Khan (The University of Agriculture, Peshawar, Pakistan), Mohib Ullah (The University of Agriculture, Peshawar, Pakistan), and Bushra Shafi (The University of Agriculture, Peshawar, Pakistan)
Copyright: © 2023 |Pages: 17
DOI: 10.4018/978-1-6684-6914-9.ch003
Abstract
Privacy quantification methods are used to quantify the knowledge the adverse search engine has obtained with and without privacy protection mechanisms. Thus, these methods calculate privacy exposure. Private web search techniques are based on many methods (e.g., proxy service, query modification, query exchange, and others). This variety of techniques prompted the researchers to evaluate their work differently. This section introduces the metrics used to evaluate user privacy (protection). Moreover, this section also introduces the metrics used to evaluate the performance of privacy attacks and theoretical evaluation approaches.
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Predicting Company Bankruptcy Using Machine Learning Techniques: A Step-by-Step Guide
Measures used to assess the performance of a machine learning model, such as accuracy, precision, recall, F1-score, and AUC-ROC.
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