Explanation

In scikit‑learn, most estimators expect a fully populated numeric matrix. If you pass an array that contains np.nan, the estimator will raise a ValueError. The library therefore treats missing values as a data‑quality issue that must be resolved before training. There are a few exceptions: the HistGradientBoostingRegressor and HistGradientBoostingClassifier can split on NaNs natively, and some newer tree‑based models have limited support. However, linear models, SVMs, K‑nearest neighbors, and most ensemble methods still require you to handle missingness yourself.

Naive Response

I would just drop any rows that contain NaN values before fitting the model. If I need a complete matrix, I might fill the missing entries with the column mean. That's sufficient for scikit‑learn.

Good Response

I would start by checking which estimator I plan to use. Since most scikit‑learn models raise an error on NaNs, I would wrap an imputation step inside a Pipeline. For numeric features I could use SimpleImputer with a median strategy, or a more sophisticated IterativeImputer (MICE) if the missingness pattern is complex. For categorical variables I would use SimpleImputer with the most_frequent strategy and optionally add a missing‑indicator column via add_indicator=True. I would also consider dropping columns that have a very high missing rate or rows with too many missing values. If I want to leverage native missing‑value handling, I could choose HistGradientBoosting, which splits on NaNs internally. Finally, I would evaluate the impact of the chosen preprocessing by comparing cross‑validated scores with and without the imputer to ensure I’m not leaking information.

Assessment

The naive answer only mentions dropping rows or simple mean imputation, which ignores the fact that many estimators cannot handle NaNs and that dropping data can hurt model performance. The good answer demonstrates a systematic workflow: it checks estimator compatibility, uses pipelines, selects appropriate imputation strategies for numeric and categorical data, adds missing‑indicators, considers column/row removal thresholds, mentions models with native support, and validates the preprocessing impact. This depth shows both technical knowledge and practical thinking that interviewers look for.

107. Missing Values in Scikit Learn

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