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NEW QUESTION: 1
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.
Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class.
Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let's make the following definitions:
"Wolf" is a positive class.
"No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative
NEW QUESTION: 2
Sie haben eine Anwendung, die Wettervorhersagedaten für externe Partner bereitstellt. Sie verwenden die Azure-API-Verwaltung zum Veröffentlichen von APIs.
Sie müssen das Verhalten der API ändern, um die folgenden Anforderungen zu erfüllen:
* Unterstützung alternativer Eingabeparameter.
* Entfernen Sie den Formatierungstext aus den Antworten.
* Bereitstellung eines zusätzlichen Kontexts für Back-End-Services.
Welche Arten von Richtlinien sollten Sie implementieren? Ziehen Sie zum Beantworten die Richtlinientypen in die richtigen Szenarien. Jeder Richtlinientyp kann einmal, mehrmals oder überhaupt nicht verwendet werden. Möglicherweise müssen Sie die geteilte Leiste zwischen den Fenstern ziehen oder einen Bildlauf durchführen, um den Inhalt anzuzeigen. HINWEIS: Jede richtige Auswahl ist einen Punkt wert.
Answer:
Explanation:
NEW QUESTION: 3
개발 팀은 현재 축적된 게임 결과를 저장하기 위해 메모리 내장 스토어를 사용하는 애플리케이션을 지원합니다. 개별 결과는 데이터베이스에 저장됩니다. 팀은 AWS로 마이그레이션 할 때 자동 크기 조정 기능을 사용해야 합니다. 팀은 이것이 일관성없는 결과를 산출 할 것이라는 것을 알고 있습니다.
팀은 누적 된 게임 결과를 BEST에 저장하여 성능에 영향을주지 않고 일관성있는 결과를 얻을 수 있도록 해야 합니까?
A. Amazon RDS
B. Amazon ElastiCache
C. Amazon S3
D. 아마존 키네시스
Answer: B