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X Val Cv

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

X Val Cv has 4 facts recorded in Dontopedia across 2 references.

4 facts·4 predicates·2 sources

Mostly:rdfs:label(1), rdf:type(1), subset of(1)

Maturity scale raw canonical shape-checked rule-derived certified

Rdfs:labelrdfs:label

  • X_val_cv[2]sourceall time · 0956e934 046c 45ee 94d8 496a65473dfc

Rdf:typerdf:type

  • Dataset[2]all time · 0956e934 046c 45ee 94d8 496a65473dfc

Subset ofsubsetOf

  • X[1]all time · D8afae17 1d41 41a0 98bd 510a77330309

Is Result ofisResultOf

  • Iloc[1]sourceall time · D8afae17 1d41 41a0 98bd 510a77330309

Inbound mentions (2)

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.

calledOnCalled on(1)

containsParameterContains Parameter(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.

4 undated
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isResultOfbeam/d8afae17-1d41-41a0-98bd-510a77330309
ex:iloc
—
labelbeam/0956e934-046c-45ee-94d8-496a65473dfc
X_val_cv
—
typebeam/0956e934-046c-45ee-94d8-496a65473dfc
ex:Dataset
—
subsetOfbeam/d8afae17-1d41-41a0-98bd-510a77330309
ex:X

References (2)

2 references
  1. [1]beam-chunk2 facts
    customctx:claims/beam/d8afae17-1d41-41a0-98bd-510a77330309
    • full textbeam-chunk
      text/plain1 KBdoc:beam/d8afae17-1d41-41a0-98bd-510a77330309
      Show excerpt
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y) # Standardize the data scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Define the …
  2. [2]beam-chunk2 facts
    customctx:claims/beam/0956e934-046c-45ee-94d8-496a65473dfc
    • full textbeam-chunk
      text/plain1 KBdoc:beam/0956e934-046c-45ee-94d8-496a65473dfc
      Show excerpt
      y_pred_cv = model.predict(X_val_cv) scores.append(accuracy_score(y_val_cv, y_pred_cv)) print(f"Cross-validation scores: {scores}") print(f"Mean CV score: {np.mean(scores):.4f}") ``` ### Explanation 1. **Data Splitting**: Split th…

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