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X Val

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

X Val has 5 facts recorded in Dontopedia across 2 references, with 1 live disagreement.

5 facts·4 predicates·2 sources·1 in dispute

Mostly:rdf:type(2), shape(1), derived from(1)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

  • Data Array[2]sourceall time · 7ef0c749 7e6a 4bc4 B3d0 D4b9ba48ae8e
  • Variable[1]all time · 16a732b3 3e07 4ba8 A721 14e165b54a5e

Shapeshape

  • validation-set-dimensions[1]all time · 16a732b3 3e07 4ba8 A721 14e165b54a5e

Derived Fromderived-from

  • X[1]all time · 16a732b3 3e07 4ba8 A721 14e165b54a5e

Extracted FromextractedFrom

  • X[2]all time · 7ef0c749 7e6a 4bc4 B3d0 D4b9ba48ae8e

Inbound 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.

called-withCalled With(1)

calledWithCalled With(1)

containsContains(1)

  • Xex:X

predictsForPredicts for(1)

splitsDataIntoSplits Data Into(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.

derived-frombeam/16a732b3-3e07-4ba8-a721-14e165b54a5e
ex:X
extractedFrombeam/7ef0c749-7e6a-4bc4-b3d0-d4b9ba48ae8e
ex:X
typebeam/7ef0c749-7e6a-4bc4-b3d0-d4b9ba48ae8e
ex:DataArray
typebeam/16a732b3-3e07-4ba8-a721-14e165b54a5e
ex:Variable
shapebeam/16a732b3-3e07-4ba8-a721-14e165b54a5e
validation-set-dimensions

References (2)

2 references
  1. customctx:claims/beam/16a732b3-3e07-4ba8-a721-14e165b54a5e
  2. [2]beam-chunk2 facts
    customctx: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

See also

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