:PROPERTIES:
:ID: b8a9886a-d349-43e5-a745-817a148c1fd8
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#+title: impl/datasets
#+filetags: :project: :knowledge: :datasets:

* impl/datasets

** Datasets with Final Results

*** CIFAR-10N (image)
- *Size*: 50,000 training images (32×32 RGB, CIFAR-10 base)
- *Noise*: clean 0% / aggre ~9% / worse ~40% (human annotator noise)
- *Source*: UCSC-REAL/cifar-10-100n — standard noisy-label benchmark (Learning with Noisy Labels, NeurIPS 2021)
- *Notes*: Positive class = automobile (class 1) vs rest; natural IR ~9:1. Three noise conditions all have final results. Labels replaced with real human annotation noise from crowdworkers.

*** Credit Card Fraud (tabular)
- *Size*: 284,807 transactions
- *Noise*: 0% (clean; real confirmed fraud labels)
- *Source*: Kaggle / ULB (Worldline + MLG research collaboration, 2013)
- *Notes*: Extreme imbalance — only 492 frauds (IR ~577:1). 30 features: V1–V28 are PCA-transformed (original features confidential), plus Time and Amount. Time/Amount rescaled; V1–V28 left as-is.

*** Bank Marketing (tabular)
- *Size*: 41,188 samples; ~48 features after one-hot encoding
- *Noise*: 0% (clean)
- *Source*: UCI Machine Learning Repository (Moro et al., 2014 — Portuguese bank direct marketing campaigns)
- *Notes*: IR ~8:1 (~11.3% positive). Target: whether client subscribed to term deposit. Mix of demographic, contact, and economic features.

*** Home Credit (tabular)
- *Size*: 307,511 loan applications; ~120 numerical features
- *Noise*: 0% (clean; proxy labels — loan repayment used as credit default indicator)
- *Source*: Kaggle Home Credit Default Risk competition (2018)
- *Notes*: IR ~11.4:1 (~8% default rate). Features from application_train.csv only (no auxiliary tables). Non-numeric and ID columns dropped; NaNs filled with 0.

*** KEEL Datasets (tabular, 12 with final results)
- *Noise*: 0% (clean — standard imbalanced learning benchmarks)
- *Source*: KEEL repository (Knowledge Extraction based on Evolutionary Learning)
- *Notes*: All small/medium tabular datasets used as standard IR benchmarks in the literature. Loaded via keel_ds Python package.

| Dataset      | Total | Pos | Neg | IR   | Notes                              |
|--------------+-------+-----+-----+------+------------------------------------|
| glass0       |   214 |  70 | 144 |  2.1 | Glass identification (UCI)         |
| glass1       |   214 |  76 | 138 |  1.8 | Glass identification (UCI)         |
| glass2       |   214 |  17 | 197 | 11.6 | Glass identification (UCI)         |
| glass6       |   214 |  29 | 185 |  6.4 | Glass identification (UCI)         |
| haberman     |   306 |  81 | 225 |  2.8 | Breast cancer survival (1958–1970) |
| iris0        |   150 |  50 | 100 |  2.0 | Iris setosa vs others              |
| new-thyroid1 |   215 |  35 | 180 |  5.1 | Thyroid function classification    |
| pima         |   768 | 268 | 500 |  1.9 | Pima Indian diabetes (UCI)         |
| vehicle2     |   846 | 218 | 628 |  2.9 | Vehicle silhouette (UCI)           |
| vowel0       |   988 |  90 | 898 | 10.0 | Vowel recognition (class 0 vs rest)|
| wisconsin    |   683 | 239 | 444 |  1.9 | Wisconsin breast cancer (UCI)      |
| yeast3       |  1484 | 163 |1321 |  8.1 | Yeast protein localization (UCI)   |

** KEEL Dataset Class Balance (full list)

Counts from =KeelDataset= loader. Ratio = false/true (imbalance factor).
Two datasets fail to load (likely missing env vars or files): =led7digit=, =page-blocks=.

| Dataset              | Total | True | False | Ratio | Runnable |
|----------------------+-------+------+-------+-------+----------|
| cleveland-0_vs_4     |   177 |   13 |   164 |  12.6 | NO       |
| ecoli-0-1_vs_5       |   240 |   20 |   220 |  11.0 | NO       |
| glass0               |   214 |   70 |   144 |   2.1 | YES      |
| glass1               |   214 |   76 |   138 |   1.8 | YES      |
| glass2               |   214 |   17 |   197 |  11.6 | YES      |
| glass4               |   214 |   13 |   201 |  15.5 | NO       |
| glass5               |   214 |    9 |   205 |  22.8 | NO       |
| glass6               |   214 |   29 |   185 |   6.4 | YES      |
| haberman             |   306 |   81 |   225 |   2.8 | YES      |
| iris0                |   150 |   50 |   100 |   2.0 | YES      |
| led7digit            |   N/A |      |       |       | NO       |
| new-thyroid1         |   215 |   35 |   180 |   5.1 | YES      |
| page-blocks          |   N/A |      |       |       | NO       |
| pima                 |   768 |  268 |   500 |   1.9 | YES      |
| vehicle2             |   846 |  218 |   628 |   2.9 | YES      |
| vowel0               |   988 |   90 |   898 |  10.0 | YES      |
| wisconsin            |   683 |  239 |   444 |   1.9 | YES      |
| yeast3               |  1484 |  163 |  1321 |   8.1 | YES      |

** Gotchas

- ISJ "Need more data" failure: cleveland (13 true), ecoli (20 true), glass4 (13), glass5 (9) — after 33% test split, too few samples in minority class for ISJ root-finding. Excluded from =run_all.sh=.
- glass2 (17 true) is borderline — watch for failures.
- led7digit and page-blocks fail to load entirely (missing dataset files or env vars).
- Credit card V1–V28 are pre-PCA (do NOT re-scale them); only Time (col 0) and Amount (col 29) are rescaled.
- Home Credit uses application_train.csv only — no auxiliary tables (bureau, previous applications, etc.) merged in.
- CIFAR-10N aggre noise rate is ~9%, worse is ~40% — large gap; both have completed final results.