MLS-C01 · Question #214
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…
The correct answer is B. Use supervised learning methods to estimate the missing values for each feature. After getting to know your data through data summaries and visualizations, you might want to transform your variables further to make them more meaningful. This is known as feature One of the common feature processing is imputing missing values to replace missing values with…
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
How the community answered
(25 responses)- A8% (2)
- B72% (18)
- C4% (1)
- D16% (4)
Explanation
After getting to know your data through data summaries and visualizations, you might want to transform your variables further to make them more meaningful. This is known as feature One of the common feature processing is imputing missing values to replace missing values with the mean or median value. It is important to understand your data before choosing a strategy for replacing missing values. While the abovementioned strategy is possible, using a supervised learning method to approximate missing values will most likely provide better results. Supervised learning applied to the imputation of missing values is an active field of research.
Topics
Community Discussion
No community discussion yet for this question.