In binary classification , one class is termed positive and the other is termed negative . The positive class is the thing or event that the model is testing for and the negative class is the other possibility. For example:
The negative class in a medical test might be “not tumor.”
The negative class in an email classifier might be “not spam.”
목차 목차 관련 글 Classification threshold 이 글을 인용: “In a binary classification, a number between 0 and 1 that converts the raw output of a logistic regression mode…” Recall (machine learning) 이 글을 인용: “False negative means that the model mistakenly predicted the negative class.” ML crash course - Classification “False negative (FN): A spam email misclassified as not-spam.” Area under the ROC curve 이 글을 인용: “A number between 0.0 and 1.0 representing a binary classification model's ability to separate positive classes …” 로지스틱 회귀 이 글을 인용: “If the predicted number is less than the classification threshold, the binary classification model predicts the…” 분류 모델 “A model that predicts the positive or negative class for a particular medical condition.” Accuracy (machine learning) “So, the accuracy formula for binary classification is as follows:” Precision (machine learning) “When the model predicted the positive class, what percentage of the predictions were correct?” Minority class (machine learning) “For example, given a dataset containing 99% negative labels and 1% positive labels, the positive labels are the…” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] Negative psychological aspects of working with experimental animals in scientific research “The aim of the study was to reveal the negative psychological aspects of using animals by scientists and to det…” Class (machine learning) “In a multi-class classification model that identifies dog breeds, the classes might be poodle, beagle, pug, and…” Downsampling (machine learning) “For example, in a class-imbalanced dataset, models tend to learn a lot about the majority class and not enough …” False positive rate “The proportion of actual negative examples for which the model mistakenly predicted the positive class.” 참음성 이 글을 인용: “An example in which the model correctly predicts the negative class.” Ground truth (machine learning) “For example, consider a binary classification model that predicts whether a student in their first year of univ…” False negative 이 글을 인용: “An example in which the model mistakenly predicts the negative class.” Machine Learning Systems “Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML…” Prediction (machine learning) “The prediction of a binary classification model is either the positive class or the negative class.” Majority class (machine learning) “For example, given a dataset containing 99% negative labels and 1% positive labels, the negative labels are the…”