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All 5 Flashcards — Data cleaning
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Question
What is data cleaning?
Answer
Finding and fixing what is **missing, duplicated, inconsistent or wrong** before training — because a model learns the faults along with everything else.
Question
Why is a recorded 0 often a missing value?
Answer
Many systems encode "not recorded" as 0. A blood pressure of 0 is impossible, so the model learns from a value that never occurred.
Question
Why can deleting rows with missing values bias a dataset?
Answer
Absence is **rarely random**. If a value is missing more often for one group, deleting those rows removes that group disproportionately.
Question
What does a duplicate record do to training?
Answer
It counts **twice**, so that record's characteristics carry double weight in what the model learns.
Question
Should outliers always be removed?
Answer
No. An **error** should be; a **genuine extreme** should not — in fraud detection the outliers are exactly the target.
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Full study notes for Data cleaning
Topic 4.2 hub
Data preprocessing
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