Dontopedia

Split Data Statement

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)

Split Data Statement has 8 facts recorded in Dontopedia across 1 reference, with 1 live disagreement.

8 facts·7 predicates·1 sources·1 in dispute

Mostly:splits into(2), rdf:type(1), has test size(1)

Maturity scale raw canonical shape-checked rule-derived certified

Inbound mentions (3)

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.

isOutputOfIs Output of(2)

containsStatementContains Statement(1)

Other facts (8)

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.

8 facts
PredicateValueRef
Splits IntoTrain Df[1]
Splits IntoTest Df[1]
Rdf:typeData Split Operation[1]
Has Test Size0.2[1]
Has Random State42[1]
PrecedesTrain Model Statement[1]
Ensures Reproducibilitytrue[1]
Creates Split Ratio80:20[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.

typebeam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
ex:DataSplitOperation
splitsIntobeam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
ex:train-df
splitsIntobeam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
ex:test-df
hasTestSizebeam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
0.2
hasRandomStatebeam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
42
precedesbeam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
ex:train-model-statement
ensuresReproducibilitybeam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
true
createsSplitRatiobeam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
80:20

References (1)

1 references
  1. ctx:claims/beam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
    • full textbeam-chunk
      text/plain1 KBdoc:beam/f64ce046-3d3f-49b8-999c-3ceaeca8f188
      Show excerpt
      # Load the data df = pd.read_csv('data.csv') # Split the data into training and testing sets train_df, test_df = df.split(test_size=0.2, random_state=42) # Train the model model = SparseModel() model.fit(train_df) # Make predictions pred

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