beginner
8 min read
Compare Imputation Strategies in a Leakage-Safe Scikit-Learn Pipeline
Fill the missing values, split the data, train the model, and watch your score climb. It feels like progress. It is usually a leak.
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Missing Data
Understanding, representing, diagnosing, and handling unavailable values and the assumptions or consequences of missingness.
Tagged articles
Missing Data articles
4 articles in this tag.
intermediate
12 min read
MCAR, MAR, and MNAR: Formal Assumptions About Missing Data
Two analysts open the same CSV. Both see the same 12% of rows with a blank in the income column. One drops those rows and reports a mean of 54,000. The…
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beginner
10 min read
Missing Data in Machine Learning: When Imputation Helps or Lies
Every beginner hits the same wall: you load a real dataset, and it is full of holes. Columns you need are dotted with NaN. Your model refuses to run. So…
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beginner
7 min read
Simulate Missingness Mechanisms and Compare Analysis Results
Here is the uncomfortable truth about missing data: in a real dataset, you never see the values that went missing. You cannot check whether your imputation…
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