MLS-C01 · Question #214
MLS-C01 Question #214: Real Exam Question with Answer & Explanation
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Question
A Machine Learning Specialist is preparing the dataset to be used for training a linear learner model in Amazon SageMaker. During exploratory data analysis, he has detected multiple feature columns that have missing values. The percentage of missing data across the whole training dataset is about 10%. The Specialist is worried that this might cause bias to his model that can lead to inaccurate results. Which approach will MOST likely yield the best result in reducing the bias caused by missing values?
Options
- ADrop the columns that include missing values because they only account for 10% of the
- BUse supervised learning methods to estimate the missing values for each feature.
- CCompute the mean of non-missing values in the same row and use the result to replace
- DCompute the mean of non-missing values in the same column and use the result to
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