By Team Hypomnema · Data
Inspect class counts and resample training records reproducibly.
Use the guide alongside the working tool.
Open Class Balance & Resampling →Explicit working sample
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B,6Inspect class counts and resample training records reproducibly. Training data only: resampling before splitting leaks examples into evaluation sets. Random under/oversampling balances every class to the smallest/largest original class. Seeded deterministic sampling; no synthetic SMOTE. Output includes source row IDs and replication counts, maximum 25,000 output rows.
Prepare the input
Inspect class counts and resample training records reproducibly.
CSV, TSV and arrays of scalar JSON objects are supported. Identifiers stay text. Empty cells are missing by default; add special missing tokens only if their meaning is certain. Use the sample to inspect the exact workflow.
Review the method
Training data only: resampling before splitting leaks examples into evaluation sets. Random under/oversampling balances every class to the smallest/largest original class. Seeded deterministic sampling; no synthetic SMOTE. Output includes source row IDs and replication counts, maximum 25,000 output rows.
Keep the useful output
Review the summary and result records. Transformation tools retain separate change, rejected-record or diagnostics datasets where relevant. Choose the dataset before exporting; a report and a cleaned dataset serve different purposes.
CSV exports protect formula-like text. JSON keeps data portable without making an Excel workbook. Reports and editable backups may contain private source values; download only what you intend to share. No automatic account storage or server processing is used.
Ready to apply this workflow?
Try Class Balance & Resampling →Explore a related workflow
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Keep your original files and review the result. Found unclear instructions? Contact Team Hypomnema.