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Question 80 - DP-100 discussion

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You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean

Missing Data.

You need to select a data cleaning method.

Which method should you use?

A.
Replace using Probabilistic PCA
Answers
A.
Replace using Probabilistic PCA
B.
Normalization
Answers
B.
Normalization
C.
Synthetic Minority Oversampling Technique (SMOTE)
Answers
C.
Synthetic Minority Oversampling Technique (SMOTE)
D.
Replace using MICE
Answers
D.
Replace using MICE
Suggested answer: A

Explanation:

Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

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asked 02/10/2024
Joan Campo
37 questions
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