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NEW QUESTION: 1
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.
Does the solution meet the goal?
A. Yes
B. No
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or
"Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Note: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
NEW QUESTION: 2
결론에 도달하기 전에 일련의 선택을 통해 사용자를 이끌 기 위해 설문지를 사용하는 전문가 시스템의 지식 기반은 다음과 같이 알려져 있습니다.
A. 데이터 흐름 다이어그램.
B. 규칙.
C. 시맨틱 그물.
D. 결정 트리.
Answer: D
Explanation:
설명:
의사 결정 트리는 설문을 사용하여 결론에 도달 할 때까지 일련의 선택을 통해 사용자를 유도합니다. 규칙은 if-then 관계의 사용을 통한 선언적 지식 표현을 나타냅니다. 시맨틱 네트는 노드가 물리적 또는 개념적 객체를 나타내는 그래프로 구성되며 호는 노드 간의 관계를 설명합니다. 시맨틱 그물은 데이터 흐름 다이어그램과 비슷하며 상속 메커니즘을 사용하여 데이터 중복을 방지합니다.
NEW QUESTION: 3
You have Microsoft SQL Server on a Microsoft Azure Virtual machine. The virtual machine has a database named DB1.
You need to identify the fragmentation percentage for the indexes of DB1.
Which dynamic management view should you query?
A. sys.dm_db_index_operational_stats
B. sys.dm_db_xtp_hash_index_stats
C. sys.dm_db_index_physical_stats
D. sys.dm_db_index_usage_stats
Answer: C
Explanation:
Explanation
References:
https://docs.microsoft.com/en-us/sql/relational-databases/system-dynamic-management-views/sys-dm-db-index-
NEW QUESTION: 4
View the Exhibit and examine the code.
Why does the code give an error on execution?
A. because the WORD_LIST parameter in out mode cannot be of a record data type
B. because the lexicon variable is not visible in procedure ADD_ENTRY
C. because the lexicon variable is not initialized in procedure wording
D. because the WORD_LIST variable is not visible in procedure wording
Answer: D
Explanation:
Explanation/Reference:
Explanation: