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Naive Bayes Model

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

Naive Bayes Model has 10 facts recorded in Dontopedia across 2 references, with 3 live disagreements.

10 facts·5 predicates·2 sources·3 in dispute

Mostly:parameter alpha values(3), has parameter alpha(3), rdf:type(2)

Maturity scale raw canonical shape-checked rule-derived certified

Rdf:typein disputerdf:type

Parameter Alpha Valuesin disputeparameterAlphaValues

  • 1[1]sourceall time · B3aa5dac A3f5 477c 922c Cef12e6cc5a9
  • 10[1]sourceall time · B3aa5dac A3f5 477c 922c Cef12e6cc5a9
  • 0.1[1]sourceall time · B3aa5dac A3f5 477c 922c Cef12e6cc5a9

Has Parameter Alphain disputehasParameterAlpha

  • 0.1[2]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
  • 10[2]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
  • 1[2]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a

Class NameclassName

  • MultinomialNB[1]sourceall time · B3aa5dac A3f5 477c 922c Cef12e6cc5a9

Rdfs:labelrdfs:label

  • MultinomialNB[2]all time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a

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.

containsContains(1)

containsModelContains Model(1)

providesClassProvides Class(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.

10 undated
—
classNamebeam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9
MultinomialNB
—
hasParameterAlphabeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
0.1
—
hasParameterAlphabeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
10
—
hasParameterAlphabeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
1
—
parameterAlphaValuesbeam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9
1
—
parameterAlphaValuesbeam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9
10
—
parameterAlphaValuesbeam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9
0.1
—
labelbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
MultinomialNB
—
typebeam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9
ex:ClassificationModel
—
typebeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:MultinomialNB

References (2)

2 references
  1. [1]beam-chunk5 facts
    customctx:claims/beam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9
    • full textbeam-chunk
      text/plain1 KBdoc:beam/b3aa5dac-a3f5-477c-922c-cef12e6cc5a9
      Show excerpt
      X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=42) # Feature extraction vectorizer = TfidfVectorizer() X_train_tfidf = vectorizer.fit_transform(X_train) X_test_tfidf = vectorizer.tr…
  2. [2]beam-chunk5 facts
    customctx:claims/beam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
    • full textbeam-chunk
      text/plain1 KBdoc:beam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
      Show excerpt
      df = pd.read_csv('data.csv') # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2, random_state=_42) # Feature extraction vectorizer = TfidfVectorizer()…

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