Build a baseline whose preprocessing can be audited
Does a more complicated classifier improve on a simple baseline under the same frozen split and training-only normalization?
Make this plan your own ↓Read, edit and export without an account. This is preparation; no run or result is claimed.
What to compare
Use identical held-out observations for paired comparisons. Select the Iris file variant explicitly, inspect Seeds feature-count metadata, and never fit normalization on the test set.
Useful outputs
- One named dataset representation, frozen stratified split and preprocessing fit boundary.
- Majority and nearest-centroid baselines with classwise metrics.
- A comparison table retaining all specified splits/seeds and uncertainty assumptions.
Inputs and prerequisites
- Payload inspection, schema validation and byte pinning remain to be done remotely.
- Package-backed experiments require qualified dependencies; no classifier has run here.
What this work would not establish
- These small datasets do not establish state-of-the-art model performance.
- License statements are scoped observations, not blanket redistribution clearance.
Start with these sources.
Iris
Flower measurements for a compact, interpretable multiclass baseline and leakage audit.
Wine
Wine chemical measurements for an interpretable small-sample classification baseline.
Seeds
Wheat-kernel geometry for an interpretable agricultural classification baseline.
3.1. Cross-validation: evaluating estimator performance
A guide to model-evaluation splits, including grouped and time-series data, nested model selection and permutation-based assessment.
3.4. Metrics and scoring: quantifying the quality of predictions
A broad scoring reference that distinguishes classification, regression, ranking and clustering metrics and their scorer interfaces.
scikit-learn
Machine-learning estimators, preprocessing tools, model-selection routines, and evaluation metrics.
Make the question your own.
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