AutoTrain MLCreator and reuse
Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian
Review the provider license or terms before reuse.
Dataset reference
Breast Cancer Wisconsin (Diagnostic)
569 diagnostic samples with 30 real-valued features for malignant-versus-benign classification.
Dataset overview
Rows569
Features30 numeric
TaskClassification
Classes2
Before training
Inspect missing values, verify target availability, remove leakage and identifiers, split before fitting transformations, and choose metrics that fit the intended decision.
Data quality checks
Confirm row meaning, duplicates, impossible values, category spelling, target balance, and whether the sample represents the population where the model will be used.
Validation planning
Use random holdout only when rows are independent. Prefer grouped, temporal, or geographic splits when related observations could cross the boundary.
Research context
Wolberg et al. (1994), Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates, Cancer Letters.
Open journal article