K Fold

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

K Fold has 6 facts recorded in Dontopedia across 2 references, with 2 live disagreements.

6 facts·3 predicates·2 sources·2 in dispute

Other facts (6)

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.

6 facts

Predicate	Value	Ref
Has Initialization Parameter	N Splits	[1]
Has Initialization Parameter	Shuffle	[1]
Has Initialization Parameter	Random State	[1]
Has Attribute	shuffle	[2]
Has Attribute	random_state	[2]
Belongs to List	Sklearn Model Selection	[1]

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.

6 undated

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belongsToListbeam/db3c4461-5bf1-4ff4-a91e-9a26c32b586a

ex:sklearn-model-selection

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hasInitializationParameterbeam/db3c4461-5bf1-4ff4-a91e-9a26c32b586a

ex:n-splits

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hasInitializationParameterbeam/db3c4461-5bf1-4ff4-a91e-9a26c32b586a

ex:shuffle

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hasInitializationParameterbeam/db3c4461-5bf1-4ff4-a91e-9a26c32b586a

ex:random-state

—

hasAttributebeam/7ef0c749-7e6a-4bc4-b3d0-d4b9ba48ae8e

shuffle

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hasAttributebeam/7ef0c749-7e6a-4bc4-b3d0-d4b9ba48ae8e

random_state

References (2)

2 references

[1]Db3c4461 5bf1 4ff4 A91e 9a26c32b586a4 facts
ctx:claims/beam/db3c4461-5bf1-4ff4-a91e-9a26c32b586a
- full textbeam-chunk
  text/plain1 KBdoc:beam/db3c4461-5bf1-4ff4-a91e-9a26c32b586a
  Show excerpt
  2. **Accuracy Score**: This is a metric from `sklearn.metrics` that computes the accuracy of the model's predictions. It is the ratio of the number of correct predictions to the total number of predictions. 3. **Cross-validation Function**…
[2]7ef0c749 7e6a 4bc4 B3d0 D4b9ba48ae8e2 facts
ctx:claims/beam/7ef0c749-7e6a-4bc4-b3d0-d4b9ba48ae8e
- full textbeam-chunk
  text/plain1 KBdoc:beam/7ef0c749-7e6a-4bc4-b3d0-d4b9ba48ae8e
  Show 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…

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Other facts (6)

Timeline

References (2)

See also

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