Handling Imbalanced Classification Data | Dataford Interview Questions - Dataford - Ace your Interview
Handling Imbalanced Classification Data
Medium
Machine Learning
Asked at 1 company1Supervised LearningRegularizationCross-Validation
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Problem
Scenario
You are training a binary classifier where the positive class is much rarer than the negative class. A model with high accuracy may still miss most of the cases you care about.
Question
How would you handle imbalanced datasets?
Example Dataset
Size·120K rows, 38 featuresTarget·Rare binary eventMissing data·12% in some behavioral fieldsClass balance·4.6% positive
Problem
Scenario
You are training a binary classifier where the positive class is much rarer than the negative class. A model with high accuracy may still miss most of the cases you care about.
Question
How would you handle imbalanced datasets?
Example Dataset
Size·120K rows, 38 featuresTarget·Rare binary eventMissing data·12% in some behavioral fieldsClass balance·4.6% positive
Your answer
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