split_data
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)
split_data is Splits the dataset into training and testing sets.
Mostly:has parameter(4), rdf:type(2), called with(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (9)
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.
hasStepHas Step(2)
- Evaluation Pipeline
ex:evaluation-pipeline - Test Algorithm
ex:test_algorithm
appliesToApplies to(1)
- Random State Consistency
ex:random_state-consistency
callsCalls(1)
- Main
main
containsFunctionContains Function(1)
- Code Structure
ex:code-structure
precedesPrecedes(1)
- Load Data
ex:load_data
step7Step7(1)
- Sequence
ex:sequence
Other facts (21)
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 |
|---|---|---|
| Has Parameter | X | [2] |
| Has Parameter | y | [2] |
| Has Parameter | test_size | [2] |
| Has Parameter | random_state | [2] |
| Rdf:type | Function | [1] |
| Rdf:type | Function | [2] |
| Called With | X | [1] |
| Called With | Y | [1] |
| Description | Splits the dataset into training and testing sets | [1] |
| Called by | Main | [1] |
| Returns | Four Split Variables | [1] |
| Produces | Training and Test Sets | [1] |
| Inverse Returns to | Main | [1] |
| Creates Training Test Data Separation | true | [1] |
| Has Default Parameter Value | 0.2 | [2] |
| Calls Function | Train Test Split | [2] |
| Has Docstring | Split the dataset into training and testing sets. | [2] |
| Splits Data | Training Set Testing Set | [2] |
| Uses Test Size | 0.2 | [2] |
| Ensures Reproducibility | random_state=42 | [2] |
| Precedes | Train Model | [2] |
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/dd6560d5-64d1-4999-ae8b-6d6edb214986- full textbeam-chunktext/plain1 KB
doc:beam/dd6560d5-64d1-4999-ae8b-6d6edb214986Show excerpt
y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) logging.debug(f"Model evaluation completed. Accuracy: {accuracy:.4f}") report = classification_report(y_test, y_pred) matrix = confusion_matri…
ctx:claims/beam/5679be66-975d-4ac3-8008-e70820051098- full textbeam-chunktext/plain1 KB
doc:beam/5679be66-975d-4ac3-8008-e70820051098Show excerpt
from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report, confusion_matrix import logging # Set up logging configuration logg…
See also
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