test_df
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
test_df has 11 facts recorded in Dontopedia across 3 references, with 3 live disagreements.
Mostly:rdf:type(3), used by(3), has column(2)
Maturity scale
raw canonical shape-checked rule-derived certifiedInbound mentions (7)
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.
hasParameterHas Parameter(2)
- Predict Labels
ex:predict-labels - Predict Labels
ex:predict-labels
usesDataUses Data(2)
- Prediction Making
ex:prediction-making - Recall Calculation
ex:recall-calculation
iteratesOverIterates Over(1)
- Iteration
ex:iteration
predictsOnPredicts on(1)
- Make Predictions Statement
ex:make-predictions-statement
splitsIntoSplits Into(1)
- Split Data Statement
ex:split-data-statement
Other facts (10)
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.
| Predicate | Value | Ref |
|---|---|---|
| Rdf:type | Data Frame | [1] |
| Rdf:type | Test Dataset | [1] |
| Rdf:type | Data Frame | [3] |
| Used by | Prediction Making | [1] |
| Used by | Recall Calculation | [1] |
| Used by | Model.predict | [2] |
| Has Column | Test Df Labels | [1] |
| Has Column | Label Column | [3] |
| Is Test Data for | Sparse Model | [2] |
| Is Output of | Split Data Statement | [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.
References (3)
ctx:claims/beam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106- full textbeam-chunktext/plain1 KB
doc:beam/b3bf4b36-b6fb-4f89-a967-2ebf362c0106Show excerpt
# Train the model model = SparseModel() model.fit(train_df) # Make predictions predictions = model.predict(test_df) # Calculate the recall score recall = recall_score(test_df['label'], predictions) print(f'Recall score: {recall:.3f}') ```…
ctx:claims/beam/f64ce046-3d3f-49b8-999c-3ceaeca8f188- full textbeam-chunktext/plain1 KB
doc:beam/f64ce046-3d3f-49b8-999c-3ceaeca8f188Show 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…
ctx:claims/beam/9669963d-f7d7-452d-a9ec-0cf09ed6be1d- full textbeam-chunktext/plain1 KB
doc:beam/9669963d-f7d7-452d-a9ec-0cf09ed6be1dShow excerpt
predictions.append(predicted_label) return predictions # Make predictions predictions = predict_labels(test_df, bm25, train_df) # Calculate the recall score recall = recall_score(test_df['label'], predictions, average='binary'…
See also
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