I am building a pipeline for my and noticed a lot of null values in my features. Should I drop these rows entirely, or is simple mean imputation sufficient to maintain the statistical integrity of my training data without introducing significant bias?
3 answers
Dropping rows is usually a last resort because you risk losing valuable information, especially if the data isn't "Missing Completely at Random." For your , simple mean imputation is often too basic and can shrink the variance of your features. I recommend using K-Nearest Neighbors (KNN) imputation or Iterative Imputers like MICE. These methods use the relationships between other features to predict the most likely value for the missing entry. This maintains the underlying distribution of your data much better and leads to more robust model performance across diverse validation sets.
Michelle, that's a great technical breakdown. But for real-time inference, doesn't KNN imputation become a massive computational bottleneck compared to just filling with a constant?
Sometimes adding a "Missing Value" indicator as a separate boolean feature can actually help the model.
Very true, Steven! Sometimes the fact that data is missing is a signal in itself. For instance, in credit scoring, an unentered field might suggest a user is trying to hide specific financial history.
You hit the nail on the head, Brian. For production systems where low latency is key, we often calculate the imputation values during training and use those fixed constants during inference. It’s a trade-off: you get the accuracy of complex imputation during training, but the speed of simple filling during the live user request.