The correct answer is B. Multivariate feature Imputer.

Here's why:

Missing At Random (MAR) happens when the missingness is not random, but where missingness can be fully accounted for by variables where there is complete information. An example is if males are less likely to fill in a depression survey but this is fully accounted for by the fact that males watch more TV on average.

In such cases, imputation techniques like Multivariate feature Imputation can be used. This technique uses the relationships between variables to fill in the missing values.

Option A, C, and D are not typically used for MAR. Imputing with a random value or with 0 can introduce a lot of noise into the data, while imputing with central tendency (mean, median, mode) doesn't take into account the relationship between variables.

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The correct answer is B. Multivariate feature Imputer.

Here's why:

Missing At Random (MAR) happens when the missingness is not random, but where missingness can be fully accounted for by variables where there is complete information. An example is if males are less likely to fill in a depression survey but this is fully accounted for by the fact that males watch more TV on average.

In such cases, imputation techniques like Multivariate feature Imputation can be used. This technique uses the relationships between variables to fill in the missing values.

Option A, C, and D are not typically used for MAR. Imputing with a random value or with 0 can introduce a lot of noise into the data, while imputing with central tendency (mean, median, mode) doesn't take into account the relationship between variables.

Knowee AI · Accepted Answer

The correct answer is B. Multivariate feature Imputer.

Here's why:

Missing At Random (MAR) happens when the missingness is not random, but where missingness can be fully accounted for by variables where there is complete information. An example is if males are less likely to fill in a depression survey but this is fully accounted for by the fact that males watch more TV on average.

In such cases, imputation techniques like Multivariate feature Imputation can be used. This technique uses the relationships between variables to fill in the missing values.

Option A, C, and D are not typically used for MAR. Imputing with a random value or with 0 can introduce a lot of noise into the data, while imputing with central tendency (mean, median, mode) doesn't take into account the relationship between variables.

33.Which of the following is imputation technique in MAR? A. Impute with random value B. Multivariate feature Imputer C. Impute with central tendency D. Impute with 0

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