Trainset
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
Trainset has 3 facts recorded in Dontopedia across 2 references, with 1 live disagreement.
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (2)
Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.
producesProduces(1)
- Data Splitting
ex:data-splitting
trainedOnTrained on(1)
- Algo
ex:algo
Other facts (3)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Training Dataset | [1] |
| Rdf:type | Training Dataset | [2] |
| Is Created From | Surprise Data | [1] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
References (2)
ctx:claims/beam/51af00c3-127f-47f4-8b3a-d5d09a4ce3ae- full textbeam-chunktext/plain1 KB
doc:beam/51af00c3-127f-47f4-8b3a-d5d09a4ce3aeShow excerpt
# Use SVD for matrix factorization algo = SVD() trainset = surprise_data.build_full_trainset() algo.fit(trainset) predictions = [] for interaction in interactions: pred = algo.predict(interaction['user_id'], …
ctx:claims/beam/ca82f6df-035e-4bb4-92d9-e1c0a1e83da2- full textbeam-chunktext/plain1 KB
doc:beam/ca82f6df-035e-4bb4-92d9-e1c0a1e83da2Show excerpt
Here's an example implementation that demonstrates how to incorporate user feedback to refine the SVD model: ```python import pandas as pd from surprise import Dataset, Reader, SVD from surprise.model_selection import train_test_split # L…
See also
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