Topic 1 Question 123
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?
Delete the rows that have missing values.
Apply feature crossing with another column that does not have missing values.
Predict the missing values using linear regression.
Replace the missing values with zeros.
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コメント(9)
- 正解だと思う選択肢: C
A no - Every row is important B no - product of other feature values with no values makes no sense to me D no - zero value would bias the model as zero distance from school has the highest value to model C yes - there is an approach using linear regression to predict missing values
👍 2pshemol2022/12/21 - 正解だと思う選択肢: C
C (not sure)
👍 2hiromi2022/12/21 One of the ways to handle missing data is deleting the rows. but question here says that every row is important. so I think another possible option could be to predict the missing value. Option C could be correct !
👍 2daran2022/12/21
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