참음성 :: akwiki

An example in which the model correctly predicts the negative class. For example, the model infers that a particular email message is not spam, and that email message really is not spam.¹

Footnotes

developers.google.com/machine-learning/glossary#bias

Accuracy (machine learning) 이 글을 인용: “TN is the number of true negatives (correct predictions).”
ML crash course - Classification 이 글을 인용: “True negative (TN): A not-spam email correctly classified as not-spam.”
Recall (machine learning) “False negative means that the model mistakenly predicted the negative class.”
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…”
Classification threshold “If the raw value is 0.7, then the model predicts the negative class.”
Positive class (machine learning) “The positive class in an email classifier might be "spam."”
분류 모델 “A model that predicts the positive or negative class for a particular medical condition.”
Area under the ROC curve “A number between 0.0 and 1.0 representing a binary classification model's ability to separate positive classes …”
Introduction to machine learning problem framing “"Classify an email as spam or not spam."”
Precision (machine learning) “True positive means the model correctly predicted the positive class.”
False negative “An example in which the model mistakenly predicts the negative class.”
이진 분류 “A model that determines whether email messages are spam (the positive class) or not spam (the negative class).”