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NEW QUESTION: 1
What is the correct one about the speed?
A. Limousine, excellent performance, can speeding
B. When there is no vehicle in front, you can drive at appropriate speed.
C. Never speeding, you need to limit the speed according to roads, conditions and other factors.
Answer: C
NEW QUESTION: 2
責任のある脅威アクターが明らかになるように、ネットワークとシステムを修復するプロセスおよび/または攻撃を再構築するプロセスはどのオプションですか？
A. 証拠収集
B. 資産属性
C. 脅威アクターの属性
D. データ分析
Answer: C
NEW QUESTION: 3
A. Option B
B. Option C
C. Option E
D. Option D
E. Option A
Answer: A
NEW QUESTION: 4
You are working on an Azure Machine Learning experiment that uses four different logistic regression algorithms. You are evaluating the algorithms based on the data in the following table.
Which model produces predictions that are the closest to the actual outcomes?
A. Model 4
B. Model 1
C. Model 3
D. Model 2
Answer: C
Explanation:
Explanation/Reference:
Explanation:
Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.
Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.
Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate- model