sklearn.datasets
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
sklearn.datasets has 9 facts recorded in Dontopedia across 4 references, with 2 live disagreements.
Mostly:rdf:type(4), contains(1), function exported(1)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (5)
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.
importsImports(2)
- Evaluation Code
ex:evaluation-code - Python Script
ex:python-script
moduleModule(1)
- Make Blobs
ex:make_blobs
packageNamePackage Name(1)
- Load Iris
ex:load-iris
sourceSource(1)
- Iris Dataset
ex:iris-dataset
Other facts (7)
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 | Python Module | [1] |
| Rdf:type | Module | [2] |
| Rdf:type | Python Module | [3] |
| Rdf:type | Python Library | [4] |
| Contains | makeClassification | [4] |
| Function Exported | make_classification | [4] |
| Exported Function | make_classification | [4] |
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 (4)
ctx:claims/beam/150d3ab0-4c59-4efc-b47d-5284bb249422- full textbeam-chunktext/plain1 KB
doc:beam/150d3ab0-4c59-4efc-b47d-5284bb249422Show excerpt
[Turn 503] Assistant: To determine which clustering algorithm performed the best based on the silhouette score, you would need to run the provided code and compare the silhouette scores for each algorithm. The silhouette score ranges from -…
ctx:claims/beam/7ef0c749-7e6a-4bc4-b3d0-d4b9ba48ae8e- full textbeam-chunktext/plain1 KB
doc:beam/7ef0c749-7e6a-4bc4-b3d0-d4b9ba48ae8eShow excerpt
X_train, X_val = X[train_index], X[val_index] y_train, y_val = y[train_index], y[val_index] # Fit the model on the training data model.fit(X_train, y_train) # Predict on the validati…
ctx:claims/beam/2372b8a2-d174-4706-8cb6-61a0fe66ec16- full textbeam-chunktext/plain1 KB
doc:beam/2372b8a2-d174-4706-8cb6-61a0fe66ec16Show excerpt
Choose algorithms that are known to be more memory-efficient. For example, decision trees and random forests are generally more memory-efficient than neural networks. ### 6. Garbage Collection Force garbage collection to free up memory whe…
ctx:claims/beam/4c194d7c-0bca-4822-b5b9-8aebf76648ff
See also
Keep researching
Missing something or suspicious of what's here? Kick off a research session — a Claude agent will investigate, cite its sources, and file new facts into a dedicated context you can review before accepting into the shared view.