Precision is the proportion of all the model’s positive classifications that are actually positive. It is a metric for classification models that answers the following question:1
“
When the model predicted the positive class , what percentage of the predictions were correct?
Here is the formula:
correctly classified actual positives everything classified as positive which means:
P r e c i s i o n = T P T P + F P where:
Precision improves as false positives decrease, while recall improves when false negatives decrease. They often show an inverse relationship, where improving one of them worsens the other. (See also: F1 score )
목차
When (not) to use
Use when it’s very important for positive predictions to be accurate.2
In an imbalanced dataset where the number of actual positives is very, very low, say 1-2 examples in total, precision is less meaningful and less useful as a metric.2
See also
Footnotes
목차 관련 글 Introduction to machine learning problem framing 이 글을 인용: “Success metrics differ from the model's evaluation metrics, like accuracy, precision, recall, or AUC.” Classification threshold “If this raw value is greater than the classification threshold, then the positive class is predicted.” ROC curve “A graph of true positive rate versus false positive rate for different classification thresholds in binary clas…” ML crash course - Linear regression “Loss is a numerical metric that describes how wrong a model's predictions are.” Introduction to Machine Learning 관련 절: Definition of [[Machine learning|ML]] 정밀도 변동 “Precision is thought to be estimated in neuronal populations throughout the brain.” Minority class (machine learning) “For example, given a dataset containing 99% negative labels and 1% positive labels, the positive labels are the…” Downsampling (machine learning) “For example, in a class-imbalanced dataset, models tend to learn a lot about the majority class and not enough …” Machine Learning Systems “Machine Learning Systems provides a systematic framework for understanding and engineering machine learning (ML…” Area under the ROC curve “A number between 0.0 and 1.0 representing a binary classification model's ability to separate positive classes …” Self-training (machine learning) “A variant of self-supervised learning that is particularly useful when all of the following conditions are true:” Negative class (machine learning) “In binary classification, one class is termed positive and the other is termed negative.” 이진 분류 “The following two machine learning models each perform binary classification:” Class (machine learning) “A classification model predicts a class.” 지도 학습 “Supervised machine learning is analogous to learning a subject by studying a set of questions and their corresp…” Generalization (machine learning) “A model's ability to make correct predictions on new, previously unseen data.” 모델 (기계학습) “In supervised machine learning, a model takes an example as input and infers a prediction as output.” 기계학습 관련 절: Generalization and OOD Iteration (machine learning) “A backward pass (backpropagation) to adjust the model's parameters based on the loss and the learning rate.” Prediction (machine learning) “The prediction of a binary classification model is either the positive class or the negative class.”