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…

The correct answer is A. Replace using Probabilistic PCA. 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…

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Question

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?

Options

AReplace using Probabilistic PCA
BNormalization
CSynthetic Minority Oversampling Technique (SMOTE)
DReplace using MICE

How the community answered

(51 responses)

A
75% (38)
B
4% (2)
C
14% (7)
D
8% (4)

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

Topics

#Data Cleaning#Missing Data Imputation#Azure Machine Learning#Probabilistic PCA

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