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NEW QUESTION: 1
特定の期間に発生したタクシーの旅に関する情報を含むデータセットがあります。
タクシーの旅の運賃を予測するには、モデルをトレーニングする必要があります。
機能として何を使用する必要がありますか？
A. データセット内のタクシーの旅の数
B. 個々のタクシーの旅の旅行ID
C. 個々のタクシーの旅の運賃
D. 個々のタクシーの移動距離
Answer: D
Explanation:
Explanation
The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.
Example:
The provided data set contains the following columns:
vendor_id: The ID of the taxi vendor is a feature.
rate_code: The rate type of the taxi trip is a feature.
passenger_count: The number of passengers on the trip is a feature.
trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don't know how long the trip would take. Thus, the trip time is not a feature and you'll exclude this column from the model.
trip_distance: The distance of the trip is a feature.
payment_type: The payment method (cash or credit card) is a feature.
fare_amount: The total taxi fare paid is the label.
Reference:
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices
NEW QUESTION: 2
Welches der folgenden Risiken ist mit einer neuen Chat-Funktion auf der Website eines Einzelhändlers am größten?
A. Produktivitätsverlust
B. Reputationsschaden
C. Datenverlust
D. Systemausfallzeit
Answer: A
NEW QUESTION: 3
Welcher Prozess ist für die Verfügbarkeit, Vertraulichkeit und Integrität von Daten verantwortlich?
A. Servicekatalogverwaltung
B. Informationssicherheitsmanagement
C. Service Asset- und Konfigurationsmanagement
D. Änderungsmanagement
Answer: B