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What is F1 Score

Chapter 8
Handbook of Research on Applications and Implementations of Machine Learning Techniques
F1 score is a combination function of precision and recall. It is used when we need to seek a balance between precision and recall.
Published in Chapter:
Machine Learning in Python: Diabetes Prediction Using Machine Learning
Astha Baranwal (VIT University, India), Bhagyashree R. Bagwe (VIT University, India), and Vanitha M (VIT University, India)
Copyright: © 2020 |Pages: 27
DOI: 10.4018/978-1-5225-9902-9.ch008
Abstract
Diabetes is a disease of the modern world. The modern lifestyle has led to unhealthy eating habits causing type 2 diabetes. Machine learning has gained a lot of popularity in the recent days. It has applications in various fields and has proven to be increasingly effective in the medical field. The purpose of this chapter is to predict the diabetes outcome of a person based on other factors or attributes. Various machine learning algorithms like logistic regression (LR), tuned and not tuned random forest (RF), and multilayer perceptron (MLP) have been used as classifiers for diabetes prediction. This chapter also presents a comparative study of these algorithms based on various performance metrics like accuracy, sensitivity, specificity, and F1 score.
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Chapter 6
Using Data Science to Predict Hotel Booking Cancellations
Measure of prediction accuracy, which is the harmonic means of precision and recall.
Published in Chapter: Using Data Science to Predict Hotel Booking Cancellations; From: Handbook of Research on Holistic Optimization Techniques in the Hospitality, Tourism, and Travel Industry
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Chapter 8
Systematic Literature Review: XAI and Clinical Decision Support
Represents the harmonic mean of precision and sensitivity in which both are maximised to the largest extent possible, given that one comes at the expense of the other. The higher the score, the better the performance.
Published in Chapter: Systematic Literature Review: XAI and Clinical Decision Support; From: Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems
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Chapter 12
Statistical Hypothesization and Predictive Modeling of Reactions to COVID-19-Induced Remote Work: Study to Understand the General Trends of Response to Pursuing Academic and Professional Commitments
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Chapter 13
Utilizing Artificial Intelligence for Text Classification in Communication Sciences: Reliability of ChatGPT Models in Turkish Texts
The F1 score is the harmonic mean of precision and recall. It provides a single score that balances both precision and recall. It's particularly useful when the classes are imbalanced, meaning one class has significantly more instances than the other. The F1 score ranges from 0 to 1, where a higher score indicates better performance.
Published in Chapter: Utilizing Artificial Intelligence for Text Classification in Communication Sciences: Reliability of ChatGPT Models in Turkish Texts; From: Design and Development of Emerging Chatbot Technology
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Chapter 13
Text Separation From Document Images: A Deep Learning Approach
F1 score is the weighted average of precision and recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution. Accuracy works best if false positives and false negatives have similar cost. If the value of false positives and false negatives are very different, it's better to look at both Precision and Recall. F1 Score = 2*(Recall * Precision) / (Recall + Precision).
Published in Chapter: Text Separation From Document Images: A Deep Learning Approach; From: Machine Learning and Deep Learning in Real-Time Applications
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Chapter 10
Deep Learning-Based Mobile Application for Plant Disease Diagnosis: A Proof of Concept With a Case Study on Tomato Plant
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