YES, IT'S VERMARCABLE
Training Data: Labeling, Splits & Evaluation Metrics Glossary
A plain-language glossary of core machine-learning data terms — training/validation/test splits, labels, features, overfitting, precision, recall and inter-annotator agreement — grounded in standard ML literature. This free platform-curated dataset helps data-labeling teams, ML engineers and reviewers align on shared evaluation vocabulary.
{
"_type": "curated_open_data",
"as_of": "2026-07",
"links": {
"canonical": "https://vermarco.com",
"docs_for_llms": "https://vermarco.com/llms.txt",
"sell_your_own": "https://vermarco.com/api/marketplace/listings",
"vertical_listings": "https://vermarco.com/api/marketplace/listings?vertical=training-data"
},
"records": [
{
"term": "Training set",
"notes": "Largest partition",
"category": "Data split",
"definition": "Data used to fit model parameters"
},
{
"term": "Validation set",
"notes": "Not used for final reporting",
"category": "Data split",
"definition": "Data used to tune hyperparameters and select models"
},
{
"term": "Test set",
"notes": "Reported on once",
"category": "Data split",
"definition": "Held-out data used only for final evaluation"
},
{
"term": "Label",
"notes": "Supervised learning",
"category": "Annotation",
"definition": "Ground-truth annotation attached to a sample"
},
{
"term": "Feature",
"notes": "Predictor variable",
"category": "Data",
"definition": "Measurable input variable used by a model"
},
{
"term": "Overfitting",
"notes": "High variance",
"category": "Modeling",
"definition": "Model fits training noise and generalizes poorly"
},
{
"term": "Epoch",
"notes": "Iterative optimization",
"category": "Training",
"definition": "One complete pass over the training dataset"
},
{
"term": "Batch size",
"notes": "Hyperparameter",
"category": "Training",
"definition": "Number of samples per gradient update"
},
{
"term": "Ground truth",
"notes": "Gold labels",
"category": "Annotation",
"definition": "Reference correct answer used for evaluation"
},
{
"term": "Inter-annotator agreement",
"notes": "Cohen's kappa",
"category": "Annotation",
"definition": "Consistency between human labelers"
},
{
"term": "Precision",
"notes": "TP / (TP + FP)",
"category": "Metrics",
"definition": "True positives divided by all predicted positives"
},
{
"term": "Recall",
"notes": "TP / (TP + FN)",
"category": "Metrics",
"definition": "True positives divided by all actual positives"
},
{
"term": "F1 score",
"notes": "2PR / (P + R)",
"category": "Metrics",
"definition": "Harmonic mean of precision and recall"
}
],
"sources": [
{
"url": "https://developers.google.com/machine-learning/glossary",
"name": "Google Machine Learning Glossary"
},
{
"url": "https://scikit-learn.org/stable/modules/model_evaluation.html",
"name": "scikit-learn model evaluation guide"
}
],
"category": "glossary",
"vertical": "training-data",
"data_note": "All records are public-domain facts compiled from the cited sources as of the asOf date. This content is authored and served by the platform itself — it is not seller data, so the marketplace's zero-storage promise about seller datasets is unaffected.",
"record_count": 13,
"what_this_is": "A platform-published open-data listing curated by Open Data Desk, the marketplace's in-house public-data seller. It is real free inventory: it counts in marketplace statistics and is purchasable for $0 through the normal purchase flow, which delivers this payload with an Ed25519-signed receipt.",
"record_schema": {
"term": "The machine-learning data term",
"notes": "Formula or clarifying note",
"category": "Category the term belongs to",
"definition": "Plain-language definition"
},
"buyer_use_cases": [
"Align data-labeling and modeling teams on shared terminology",
"Author annotation guidelines and evaluation documentation"
]
}The full dataset is delivered after purchase. Fingerprint: sha256:10469d2dcc7dbe77a8cbd4aaf45633e3bcf38de6d9c9b2f252be48d16ec46e79
No answered questions yet — ask the seller anything about this listing.
Data is contributed by independent third-party sellers. Vertical Marketplace facilitates the transaction; sellers keep 95% on everyday sales from $20 to $49,999.99 under the year-one founding rate locked through 2027-06-30 (full schedule: GET /api/meta). Prohibited content (digital keys/licenses/game codes, and health data the seller does not own — e.g. patient records) is not permitted; individuals may sell their own personal health data only via the signed Health Data Consent Flow. See /terms.
- Use the purchased data for your own commercial and non-commercial work
- Create derivative analyses, models, and works from the data
- No reselling or re-listing the purchased data on this or any other marketplace
- No redistributing the raw data payload as-is to third parties
- Exclusive listings are sold to a single buyer and delisted on purchase
- Limited listings are sold to a capped number of buyers and delisted once sold out
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