에 의해 게시 Amato
1. Sensitivity (also called the true positive rate, or the recall rate in some fields) measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition).
2. A perfect predictor would be described as 100% sensitive (i.e. predicting all people from the sick group as sick) and 100% specific (i.e. not predicting anyone from the healthy group as sick); however, theoretically any predictor will possess a minimum error bound known as the Bayes error rate.
3. Specificity measures the proportion of negatives which are correctly identified as such (e.g. the percentage of healthy people who are correctly identified as not having the condition, sometimes called the true negative rate).
4. For example: in an airport security setting in which one is testing for potential threats to safety, scanners may be set to trigger on low-risk items like belt buckles and keys (low specificity), in order to reduce the risk of missing objects that do pose a threat to the aircraft and those aboard (high sensitivity).
5. Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as classification function.
6. These two measures are closely related to the concepts of type I and type II errors.
7. For any test, there is usually a trade-off between the measures.
8. This trade-off can be represented graphically as a receiver operating characteristic curve.
또는 아래 가이드를 따라 PC에서 사용하십시오. :
PC 버전 선택:
소프트웨어 설치 요구 사항:
직접 다운로드 가능합니다. 아래 다운로드 :
설치 한 에뮬레이터 애플리케이션을 열고 검색 창을 찾으십시오. 일단 찾았 으면 Sensitivity and specificity 검색 막대에서 검색을 누릅니다. 클릭 Sensitivity and specificity응용 프로그램 아이콘. 의 창 Sensitivity and specificity Play 스토어 또는 앱 스토어의 스토어가 열리면 에뮬레이터 애플리케이션에 스토어가 표시됩니다. Install 버튼을 누르면 iPhone 또는 Android 기기 에서처럼 애플리케이션이 다운로드되기 시작합니다. 이제 우리는 모두 끝났습니다.
"모든 앱 "아이콘이 표시됩니다.
클릭하면 설치된 모든 응용 프로그램이 포함 된 페이지로 이동합니다.
당신은 아이콘을 클릭하십시오. 그것을 클릭하고 응용 프로그램 사용을 시작하십시오.
다운로드 Sensitivity and specificity Mac OS의 경우 (Apple)
| 다운로드 | 개발자 | 리뷰 | 평점 |
|---|---|---|---|
| $0.99 Mac OS의 경우 | Amato | 0 | 1 |
Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as classification function. Sensitivity (also called the true positive rate, or the recall rate in some fields) measures the proportion of actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition). Specificity measures the proportion of negatives which are correctly identified as such (e.g. the percentage of healthy people who are correctly identified as not having the condition, sometimes called the true negative rate). These two measures are closely related to the concepts of type I and type II errors. A perfect predictor would be described as 100% sensitive (i.e. predicting all people from the sick group as sick) and 100% specific (i.e. not predicting anyone from the healthy group as sick); however, theoretically any predictor will possess a minimum error bound known as the Bayes error rate. For any test, there is usually a trade-off between the measures. For example: in an airport security setting in which one is testing for potential threats to safety, scanners may be set to trigger on low-risk items like belt buckles and keys (low specificity), in order to reduce the risk of missing objects that do pose a threat to the aircraft and those aboard (high sensitivity). This trade-off can be represented graphically as a receiver operating characteristic curve.
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