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What is Confusion Matrix

Chapter 89
Encyclopedia of Data Science and Machine Learning
A table of actual and predicted classes such that the intersection of said classes statistically define the corresponding level of confusion.
Published in Chapter:
Machine Learning Approach to Art Authentication
Bryan Todd Dobbs (University of North Carolina at Charlotte, USA) and Zbigniew W. Ras (University of North Carolina at Charlotte, USA)
Copyright: © 2023 |Pages: 14
DOI: 10.4018/978-1-7998-9220-5.ch089
Abstract
The popularity of machine learning algorithms produced numerous applications in computer vision in the past 10 years. One application is art authentication, which assures that a piece of art is created by an artist. The models produced by machine learning algorithms provide an objective measure to authenticate an artist to their artwork collection. This article discusses an experiment using the residual neural network machine learning algorithm. This experiment demonstrates how a computer can distinguish between 34 and 958 artists with various degrees of confidence.
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Chapter 58
Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods
The performance of the classification model is given by confusion matrix in the form of table. The dataset is a test set whose true values are known.
Published in Chapter: Characterization of Elevated Tumor Markers in Diagnosis of HCC Using Data Mining Methods; From: Encyclopedia of Information Science and Technology, Fifth Edition
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Chapter 2
Enhanced Footsteps Generation Method for Walking Robots Based on Convolutional Neural Networks
A matrix which shows the distribution of categorizations performed by the network on a labeled dataset, where the correct categorizations lie on the main diagonal.
Published in Chapter: Enhanced Footsteps Generation Method for Walking Robots Based on Convolutional Neural Networks; From: Handbook of Research on Deep Learning Innovations and Trends
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Chapter 11
A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks
It is a statistical classification of a problem which is used for performance visualization.
Published in Chapter: A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks; From: Handbook of Research on Library Response to the COVID-19 Pandemic
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Chapter 13
An IoT-Based Earthquake Warning System for Smart Cities
A matrix that illustrates the correct and incorrect predictions in classification of classifier for each class.
Published in Chapter: An IoT-Based Earthquake Warning System for Smart Cities; From: Handbook of Research on Implementation and Deployment of IoT Projects in Smart Cities
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Chapter 12
Machine Learning and Its Application in Monitoring Diabetes Mellitus
A confusion matrix is a two-dimensional table depicting summary of classification or prediction outcomes produced by applying classification algorithm on the given dataset. The accurate and inaccurate predictions produced with respect to each class are summarized with their count values. It gives the insight of the errors being made during classification process by a classifier.
Published in Chapter: Machine Learning and Its Application in Monitoring Diabetes Mellitus; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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Chapter 16
Leveraging Wi-Fi Big Data Streams to Support COVID-19 Contact Tracing
A table that summarizes the performance of a classification model on a set of labeled data. It compares predictions to actuals and summarizes the model’s performance in terms of True Positives, True Negatives, False Positives, and False Negatives.
Published in Chapter: Leveraging Wi-Fi Big Data Streams to Support COVID-19 Contact Tracing; From: Encyclopedia of Data Science and Machine Learning
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Chapter 11
Educational Software Based on Matlab GUIs for Neural Networks Courses
A table that allows the visualization of a classification algorithm performance. It is formed by two rows and two columns that reports the number of successes/failures in the positive/negative class.
Published in Chapter: Educational Software Based on Matlab GUIs for Neural Networks Courses; From: Handbook of Research on Computational Simulation and Modeling in Engineering
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Chapter 10
Privacy Preservation of Image Data With Machine Learning
A matrix that visualizes classification algorithm effectiveness using the information in the matrix. It analyses the projected categorization in the form of true positive, false positive, true negative, and false negative information against the data used for the classification.
Published in Chapter: Privacy Preservation of Image Data With Machine Learning; From: Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
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Chapter 4
Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection
Matrix used to determine the performance of the classification models. It shows the predicted and actual class assignments along with the total number of class predictions.
Published in Chapter: Convolutional Neural Networks and Deep Learning Techniques for Glass Surface Defect Inspection; From: Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things
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Chapter 7
Multivariate Time Series Forecasting of Rainfall Using Machine Learning
The way to present the output of the classifier is called as confusion matrix. It enables the user to understand how the classifier performed in terms of various evaluation parameters such as accuracy, precision, and F1 score.
Published in Chapter: Multivariate Time Series Forecasting of Rainfall Using Machine Learning; From: Artificial Intelligence of Things for Weather Forecasting and Climatic Behavioral Analysis
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Chapter 5
Approximation of Hate Detection Processes in Spanish and Other Non-Anglo-Saxon Languages
This shows the predictions made by the model comparing them with the actual results, which allows knowing how many times the model is correct and how many times it is wrong in each of the classes that are being evaluated. In technical terms, a confusion matrix shows good results when high precision and sensitivity are observed in the classification of the data. That is, when most of the predictions made by the model are correct and there is a minimum number of false positives and false negatives in the data classification.
Published in Chapter: Approximation of Hate Detection Processes in Spanish and Other Non-Anglo-Saxon Languages; From: News Media and Hate Speech Promotion in Mediterranean Countries
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Chapter 4
Scientific Applications of Machine Learning Algorithms
Machine learning technique for result evaluation using optimization metrics.
Published in Chapter: Scientific Applications of Machine Learning Algorithms; From: Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms
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Chapter 4
On the Evaluation of Early Warning Models for Financial Crises
A table with two columns referring to the outcomes, e.g. ‘crisis’ and ‘no crisis’, and two rows referring to the predictions, e.g. ‘warning issued’ and ‘no warning issued’. Each observation, e.g. country-period, under evaluation falls in one of the four quadrants of the matrix.
Published in Chapter: On the Evaluation of Early Warning Models for Financial Crises; From: Handbook of Research on Financial and Banking Crisis Prediction through Early Warning Systems
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Chapter 3
Learning Framework for Real-World Facial Emotion Recognition
The confusion matrix is a cross table that counts the instances of the true/actual classification and the anticipated classification within two classes. Columns are used to show model predictions, while rows are used to show the actual classifications, in order to maintain consistency. The amount of times one rater agreed with the other can be found on a diagonal from top left to bottom right, which is where correctly categorised things can be discovered. Using a confusion matrix, the accuracy of the actual labels in comparison to those anticipated is evaluated.
Published in Chapter: Learning Framework for Real-World Facial Emotion Recognition; From: AI-Enabled Social Robotics in Human Care Services
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Chapter 446
Neural Networks for Automobile Insurance Pricing
Contains information about actual and predicted classifications done by a classification system.
Published in Chapter: Neural Networks for Automobile Insurance Pricing; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 6
Calibration of Machine Learning Models
a visual way of showing the recount of cases of the predicted classes and their actual values. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class.
Published in Chapter: Calibration of Machine Learning Models; From: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
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Chapter 369
Estimating Emotions Using Geometric Features from Facial Expressions
Table to visualize the performance of the supervised machine learning algorithm and the diagonal shows the prediction accuracy of the algorithm.
Published in Chapter: Estimating Emotions Using Geometric Features from Facial Expressions; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 16
The Clinical Utility of Psychometric Tests: A Real-Data Approach From a Study Including Children With ADHD
A table in a specific format that is used in classification analysis. In this table, two (or more) variables are jointly analyzed. Rows are used to present the test result whereas columns present the clinical condition status or a gold standard result.
Published in Chapter: The Clinical Utility of Psychometric Tests: A Real-Data Approach From a Study Including Children With ADHD; From: Handbook of Research on Critical Issues in Special Education for School Rehabilitation Practices
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Chapter 10
TempClass: Implicit Temporal Queries Classifier
Is a table with two rows and two columns that reports the number of false positives, false negatives, true positives, and true negatives. It shows for each pair of classes <c1, c2>, how many documents from c1 were incorrectly assigned to c2.
Published in Chapter: TempClass: Implicit Temporal Queries Classifier; From: Handbook of Research on Contemporary Perspectives on Web-Based Systems
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Chapter 165
Performance Measures and RTB Optimization
A 2×2 matrix that indicates the true/false positive/negative in a binary prediction. It is the most applied statistics to evaluate the binary prediction result.
Published in Chapter: Performance Measures and RTB Optimization; From: Encyclopedia of Business Analytics and Optimization
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Chapter 80
Art Innovative Systems for Value Tagging
A table that represents the error rate of a classifier.
Published in Chapter: Art Innovative Systems for Value Tagging; From: Encyclopedia of Organizational Knowledge, Administration, and Technology
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Chapter 7
Predictive Healthcare Web Analytics Using Machine Learning
A confusion matrix is a table that shows how well a classification model (or “classifier”) performs on a set of test data for which the real values are known.
Published in Chapter: Predictive Healthcare Web Analytics Using Machine Learning; From: Instructional Design Exemplars in eHealth and mHealth Education Interventions
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Chapter 9
Use of Data Analytics to Increase the Efficiency of Last Mile Logistics for Ecommerce Deliveries
A table that captures the performance of a classification algorithm on a dataset for which the observed values are known.
Published in Chapter: Use of Data Analytics to Increase the Efficiency of Last Mile Logistics for Ecommerce Deliveries; From: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
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