Removing the whole line
Creating sub-model to predict those features
Using an automatic strategy to input them according to the other known values
All above
Answer is Right!
Answer is Wrong!
The correct answer is: A. Removing the whole line.
Removing the whole line is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.
- Creating sub-model to predict those features is a good option when the number of missing features is not too high and the dataset is not too large. This approach can help to improve the accuracy of the predictions.
- Using an automatic strategy to input them according to the other known values is another good option when the number of missing features is not too high. This approach can help to reduce the number of missing values and improve the accuracy of the predictions.
However, when the number of missing features is high, it is better to remove the whole line. This is because the predictions for the lines with missing features are likely to be inaccurate. Removing the lines with missing features will improve the accuracy of the overall predictions.