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Everything about Evaluation of binary classifiers
Quantitative measurement of accuracy
Evaluation of a binary classifier typically assigns a numerical value, or values, to a classifier that represent its accuracy. An example is error rate, which measures how frequently the classifier makes a mistake. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Evaluation of binary classifiersA 5-minute orientation
- Skim a recent paper: Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic CapabilitiesInfluential work since 2022
- Pick up a classic book: Gaussian processes for machine learningThe most-reprinted book on the subject
- Read the landmark paper: The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluationCited 5,773 times
- Go sideways: Binary classificationA closely related topic
- Go sideways: Precision and recallA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
An empirical evaluation of the performance of binary classifiers in the prediction of credit ratings changes
Evaluating Binary Classifiers: Extending the Efficiency Index
Performance Evaluation of Error-Correcting Output Coding Based on Noisy and Noiseless Binary Classifiers
Evaluating the accuracy of binary classifiers for geomorphic applications
Evaluating Binary Outcome Classifiers Estimated from Survey Data
Federated Calibration and Evaluation of Binary Classifiers
🆕 Influential since 2022
Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities
Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers
Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning
Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes
Detecting Cry in Daylong Audio Recordings Using Machine Learning: The Development and Evaluation of Binary Classifiers
📚 Books worth reading
Gaussian processes for machine learningCarl Edward Rasmussen · 2005
Introduction to information retrievalChristopher D. Manning, Prabhakar Raghavan · 2008
Intelligent Information and Database SystemsAli Selamat · 2013- 📕Applied data miningGuandong Xu · 2013
- 📕AIChE equipment testing procedureAmerican Institute of Chemical Engineers. Equipment Testing Procedures Committee · 1980
Intelligent Information and Database SystemsAli Selamat · 2013
ICICS 2017 · 2017
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Binary classification Binary classification is the task of putting things into one of two categories (each called a class). As such, it is the simplest form of the general task…
- Precision and recall In classification, precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also…
- P4-metric The P4 metric (also known as FS or Symmetric F) enables performance evaluation of a binary classifier. The P4 metric is calculated from precision, recall…
- Youden's J statistic Youden's J statistic (also called Youden's index) is a single statistic that captures the performance of a dichotomous diagnostic test. In meteorology…
- Sensitivity and specificity In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical…
- F-score In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It…
- Phi coefficient In statistics, the phi coefficient, also known as the mean square contingency coefficient or Yule coefficient of correlation and commonly denoted by φ…
- Positive and negative predictive values The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic…