Dontopedia

Training Testing Split

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-11.)

Training Testing Split has 23 facts recorded in Dontopedia across 3 references, with 4 live disagreements.

23 facts·9 predicates·3 sources·4 in dispute

Mostly:produces(10), splits(3), has parameter(3)

Maturity scale raw canonical shape-checked rule-derived certified

Producesin disputeproduces

  • X Train[1]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
  • X Test[1]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
  • Y Train[1]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
  • Y Test[1]sourceall time · 0daa7c15 B2c7 44ef A5e9 390bf6864c0a
  • Train Dataframe[2]sourceall time · A2616d4b 38c9 4c2c 832f D576e35ce8b4
  • Test Dataframe[2]sourceall time · A2616d4b 38c9 4c2c 832f D576e35ce8b4
  • Train Text[3]sourceall time · 48adae40 4bfc 4307 B82a A3732c282daf
  • Test Text[3]sourceall time · 48adae40 4bfc 4307 B82a A3732c282daf
  • Train Labels[3]all time · 48adae40 4bfc 4307 B82a A3732c282daf
  • Test Labels[3]all time · 48adae40 4bfc 4307 B82a A3732c282daf

Inbound 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.

isOutputOfIs Output of(4)

containsContains(1)

containsStepContains Step(1)

describesDescribes(1)

isUsedInIs Used in(1)

precedesPrecedes(1)

Other facts (13)

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.

13 facts
PredicateValueRef
SplitsText Column[1]
SplitsLabel Column[1]
SplitsDataframe Df[2]
Has ParameterTest Size[1]
Has ParameterTest Size Parameter[2]
Has ParameterRandom State Parameter[2]
Rdf:typeData Split Operation[1]
Rdf:typeData Partitioning[3]
Operates onDf[1]
Has Test Size0.2[1]
Has Random State_42[1]
PrecedesFeature Extraction[1]
UsesTrain Test Split[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.

23 undated
—
typebeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:DataSplitOperation
—
operatesOnbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:df
—
producesbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:X-train
—
producesbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:X-test
—
producesbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:y-train
—
producesbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:y-test
—
hasTestSizebeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
0.2
—
hasRandomStatebeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
_42
—
splitsbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:text-column
—
splitsbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:label-column
—
hasParameterbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:test-size
—
precedesbeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:feature-extraction
—
usesbeam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4
ex:train_test_split
—
hasParameterbeam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4
ex:test-size-parameter
—
hasParameterbeam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4
ex:random-state-parameter
—
splitsbeam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4
ex:dataframe-df
—
producesbeam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4
ex:train-dataframe
—
producesbeam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4
ex:test-dataframe
—
typebeam/48adae40-4bfc-4307-b82a-a3732c282daf
ex:DataPartitioning
—
producesbeam/48adae40-4bfc-4307-b82a-a3732c282daf
ex:train-text
—
producesbeam/48adae40-4bfc-4307-b82a-a3732c282daf
ex:test-text
—
producesbeam/48adae40-4bfc-4307-b82a-a3732c282daf
ex:train-labels
—
producesbeam/48adae40-4bfc-4307-b82a-a3732c282daf
ex:test-labels

References (3)

3 references
  1. [1]0daa7c15 B2c7 44ef A5e9 390bf6864c0a12 facts
    ctx: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()…
  2. [2]A2616d4b 38c9 4c2c 832f D576e35ce8b46 facts
    ctx:claims/beam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4
    • full textbeam-chunk
      text/plain1 KBdoc:beam/a2616d4b-38c9-4c2c-832f-d576e35ce8b4
      Show excerpt
      # Split the data into training and testing sets train_df, test_df = train_test_split(df, test_size=0.2, random_state=_) # Define a function to tokenize the data def tokenize_data(tokenizer, texts): return tokenizer(texts.tolist(), trun…
  3. [3]48adae40 4bfc 4307 B82a A3732c282daf5 facts
    ctx:claims/beam/48adae40-4bfc-4307-b82a-a3732c282daf
    • full textbeam-chunk
      text/plain1 KBdoc:beam/48adae40-4bfc-4307-b82a-a3732c282daf
      Show excerpt
      Would you like to proceed with these steps or do you have any specific questions about any part of the process? [Turn 10576] User: Sure, let's start by experimenting with NLTK and spaCy to see which one works better for my spelling correct…

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