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NEW QUESTION: 1
For promoting maintenance, which approach is MOST appropriate?
A. Discontinue the schedule of reinforcement used in the training environment.
B. Use a reinforcement schedule that most closely approximates the schedule in the natural environment.
C. Implement a token economy including point gains and point losses for use in both the training setting and the natural environment.
D. Continually reinforce the behavior in the natural environment.
Answer: B
NEW QUESTION: 2
管理者は、組織内のすべてのユーザーとセグメントを共有したいと考えています。
すべてのユーザーとセグメントを共有するための2つのベストプラクティスは何ですか？ （2つ選択してください。）
A. セグメントをExperienceCloudと共有する
B. セグメントにタグを付ける
C. セグメントのコピーを作成して共有する
D. セグメントをすべてのグループと個別に共有する
E. セグメントの承認
Answer: C,E
NEW QUESTION: 3
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