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NEW QUESTION: 1
When making a decision, for a cost or revenue to be classified as "relevant" it must be:
(a) Incremental
(b) Notional
(c) Cash
(d) Future
A. (a), (b) and (d) only
B. (b), (c) and (d) only
C. (a), (c) and (d) only
D. (a), (b) and (c) only
Answer: C

NEW QUESTION: 2
A nurse at outpatient clinic is returning phone calls that have been made to the clinic. Which of the following calls should have the highest priority for medical intervention?
A. A middle-aged patient reports, "My knee is still hurting from the TKR."
B. A young female reports, "I think I sprained my ankle about 2 weeks ago."
C. A patient that received an upper extremity cast yesterday reports, "I can't feel my fingers in my right hand today."
D. A home health patient reports, "I am starting to have breakdown of my heels."
Answer: C
Explanation:
The patient experiencing neurovascular changes should have the highest priority. Pain following a TKR is normal, and breakdown over the heels is a gradual process. Moreover, a subacute ankle sprain is almost never a medical emergency.

NEW QUESTION: 3
二項分類モデルを作成して、人が病気にかかっているかどうかを予測します。
考えられる分類エラーを検出する必要があります。
説明ごとにどのエラータイプを選択する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：正しい選択はそれぞれ1ポイントの価値があります。

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