cross_validate

as donto believed on

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

cross_validate has 64 facts recorded in Dontopedia across 6 references, with 11 live disagreements.

64 facts·34 predicates·6 sources·11 in dispute

Mostly:has parameter(11), purpose(4), rdf:type(4)

Has Parameterin dispute`hasParameter`

Model Parameter^[2]sourceall time · 1b7907ef C385 4c48 Be99 C59a88201518
X Parameter^[2]sourceall time · 1b7907ef C385 4c48 Be99 C59a88201518
Y Parameter^[2]sourceall time · 1b7907ef C385 4c48 Be99 C59a88201518
K Parameter^[2]sourceall time · 1b7907ef C385 4c48 Be99 C59a88201518
model^[5]sourceall time · 7ef0c749 7e6a 4bc4 B3d0 D4b9ba48ae8e
X^[5]sourceall time · 7ef0c749 7e6a 4bc4 B3d0 D4b9ba48ae8e
y^[5]sourceall time · 7ef0c749 7e6a 4bc4 B3d0 D4b9ba48ae8e
k^[5]all time · 7ef0c749 7e6a 4bc4 B3d0 D4b9ba48ae8e
Model Parameter^[6]all time · 16a732b3 3e07 4ba8 A721 14e165b54a5e
X Parameter^[6]all time · 16a732b3 3e07 4ba8 A721 14e165b54a5e

Inbound mentions (12)

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.

`usedBy`Used by(5)

Accuracy Scoreex:accuracy-score
Accuracy Score Functionex:accuracy-score-function
K Foldex:KFold
K Fold Classex:KFold-class
Np Mean Functionex:np-mean-function

`calledWith`Called With(1)

K Fold Instanceex:KFold-instance

`calledWithin`Called Within(1)

Accuracy Score Functionaccuracy-score-function

`calls`Calls(1)

Example Usageex:example-usage

`containsFunctionDefinition`Contains Function Definition(1)

Code Blockex:code-block

`demonstrates`Demonstrates(1)

Example Usageex:example-usage

`inputFor`Input for(1)

Datasetex:dataset

`partOf`Part of(1)

K Foldex:KFold

Other facts (50)

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.

50 facts

Predicate	Value	Ref
Purpose	Model Evaluation	[1]
Purpose	Model Evaluation	[2]
Purpose	evaluate machine learning models	[4]
Purpose	evaluate-ML-model-performance	[6]
Rdf:type	Python Function	[2]
Rdf:type	Function	[3]
Rdf:type	Function	[5]
Rdf:type	Function	[6]
Has Step	Loop Through Folds	[4]
Has Step	Fit and Predict	[4]
Has Step	Store Scores	[4]
Has Step	Return Mean Score	[4]
Returns	Mean Accuracy Score	[4]
Returns	mean-accuracy	[5]
Returns	Scores Array	[6]
Called With	Model Parameter	[6]
Called With	X Parameter	[6]
Called With	Y Parameter	[6]
Creates Variable	Kf Variable	[2]
Creates Variable	Scores Variable	[2]
Benefit	deepen knowledge of cross-validation	[4]
Benefit	improve ability to evaluate machine learning models	[4]
Enables	Deepen Knowledge	[4]
Enables	Improve Evaluation Ability	[4]
Implements	cross-validation	[5]
Implements	k-fold-cross-validation	[5]
Uses Class	Kfold Class	[2]
Contains Loop	For Loop	[2]
Is Incomplete	true	[2]
Defined As	Python Function	[3]
Has Signature	Model X Y K Signature	[3]
Has Example Usage	Iris Dataset Example	[4]
Iterates Over	Folds	[4]
Requires	Dataset	[4]
Used for	Model Evaluation	[4]
Produces	Cross Validation Score	[4]
Contains	For Loop	[5]
Has Return Type	float	[5]
Defined in	code-block	[5]
Returns Type	float	[5]
Part of	scikit-learn-ecosystem	[5]
Uses	K Fold Class	[6]
Defined in	Code Snippet	[6]
Implementation	code-snippet	[6]
Initializes	scores-list	[6]
Iterates Over	folds	[6]
Function Signature	cross_validate(model, X, y)	[6]
Contains Loop	fold-iteration	[6]
Parameter Types	model, X, y	[6]
Return Type	float (mean accuracy)	[6]

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.

64 undated

—

purposebeam/d20f04e6-ac24-40a3-ba7d-a928d5401600

ex:model-evaluation

—

typebeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:python-function

—

labelbeam/1b7907ef-c385-4c48-be99-c59a88201518

cross_validate

—

hasParameterbeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:model-parameter

—

hasParameterbeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:X-parameter

—

hasParameterbeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:y-parameter

—

hasParameterbeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:k-parameter

—

usesClassbeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:kfold-class

—

createsVariablebeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:kf-variable

—

createsVariablebeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:scores-variable

—

containsLoopbeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:for-loop

—

isIncompletebeam/1b7907ef-c385-4c48-be99-c59a88201518

true

—

purposebeam/1b7907ef-c385-4c48-be99-c59a88201518

ex:model-evaluation

—

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

ex:Function

—

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

ex:python-function

—

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

ex:model-X-y-k-signature

—

hasStepbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:loop-through-folds

—

hasStepbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:fit-and-predict

—

hasStepbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:store-scores

—

hasStepbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:return-mean-score

—

hasExampleUsagebeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:iris-dataset-example

—

purposebeam/2e6d4246-fcc3-4855-b040-d7674feb705a

evaluate machine learning models

—

benefitbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

deepen knowledge of cross-validation

—

benefitbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

improve ability to evaluate machine learning models

—

labelbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

cross_validate function

—

returnsbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:mean-accuracy-score

—

enablesbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:deepen-knowledge

—

enablesbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:improve-evaluation-ability

—

iteratesOverbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:folds

—

requiresbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:dataset

—

usedForbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:model-evaluation

—

producesbeam/2e6d4246-fcc3-4855-b040-d7674feb705a

ex:cross-validation-score

—

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

ex:Function

—

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

model

—

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

—

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

—

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

ex:for-loop

—

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

mean-accuracy

—

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

—

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

cross-validation

—

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

float

—

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

code-block

—

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

k-fold-cross-validation

—

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

float

—

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

scikit-learn-ecosystem

—

typebeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:Function

—

hasParameterbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:model-parameter

—

hasParameterbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:X-parameter

—

hasParameterbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:y-parameter

—

returnsbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:scores-array

—

usesbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:KFold-class

—

labelbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

cross_validate

—

purposebeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

evaluate-ML-model-performance

—

called-withbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:model-parameter

—

called-withbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:X-parameter

—

called-withbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:y-parameter

—

defined-inbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

ex:code-snippet

—

implementationbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

code-snippet

—

initializesbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

scores-list

—

iterates-overbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

folds

—

function-signaturebeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

cross_validate(model, X, y)

—

contains-loopbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

fold-iteration

—

parameter-typesbeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

model, X, y

—

return-typebeam/16a732b3-3e07-4ba8-a721-14e165b54a5e

float (mean accuracy)

References (6)

6 references

[1]D20f04e6 Ac24 40a3 Ba7d A928d54016001 fact
ctx:claims/beam/d20f04e6-ac24-40a3-ba7d-a928d5401600
[2]1b7907ef C385 4c48 Be99 C59a8820151812 facts
ctx:claims/beam/1b7907ef-c385-4c48-be99-c59a88201518
- full textbeam-chunk
  text/plain1 KBdoc:beam/1b7907ef-c385-4c48-be99-c59a88201518
  Show excerpt
  - The `allowed_exceptions` parameter allows you to specify which exceptions should trigger a retry. By default, it catches all exceptions, but you can customize it to catch only specific exceptions like `MetricCalcError`. - The `time.sleep`…
[3]Db3c4461 5bf1 4ff4 A91e 9a26c32b586a3 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**…
[4]2e6d4246 Fcc3 4855 B040 D7674feb705a16 facts
ctx:claims/beam/2e6d4246-fcc3-4855-b040-d7674feb705a
- full textbeam-chunk
  text/plain1 KBdoc:beam/2e6d4246-fcc3-4855-b040-d7674feb705a
  Show excerpt
  2. **Loop Through Folds**: The `kf.split(X)` method generates indices for the training and validation sets. For each fold, the data is split into `X_train`, `X_val`, `y_train`, and `y_val`. 3. **Fit and Predict**: The model is fitted on th…
[5]7ef0c749 7e6a 4bc4 B3d0 D4b9ba48ae8e13 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…
[6]16a732b3 3e07 4ba8 A721 14e165b54a5e19 facts
ctx:claims/beam/16a732b3-3e07-4ba8-a721-14e165b54a5e

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.

cross_validate

Has Parameterin disputehasParameter

Inbound mentions (12)

usedByUsed by(5)

calledWithCalled With(1)

calledWithinCalled Within(1)

callsCalls(1)

containsFunctionDefinitionContains Function Definition(1)

demonstratesDemonstrates(1)

inputForInput for(1)

partOfPart of(1)

Other facts (50)

Timeline

References (6)

See also

Keep researching

Has Parameterin dispute`hasParameter`

`usedBy`Used by(5)

`calledWith`Called With(1)

`calledWithin`Called Within(1)

`calls`Calls(1)

`containsFunctionDefinition`Contains Function Definition(1)

`demonstrates`Demonstrates(1)

`inputFor`Input for(1)

`partOf`Part of(1)