Test Features (TF-IDF)
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-09.)
Test Features (TF-IDF) has 12 facts recorded in Dontopedia across 6 references, with 2 live disagreements.
Mostly:rdf:type(5), derived from(2), shape(1)
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.
consistsOfConsists of(2)
- Test Data
ex:test_data - Testing Set
ex:testing_set
usesVariableUses Variable(2)
- Code Block
ex:code-block - Model Predict
ex:model-predict
appliedOnApplied on(1)
- Predict Method
ex:predict_method
appliedToApplied to(1)
- Prediction Operation
ex:prediction-operation
calledWithCalled With(1)
- Best Model
ex:best_model
Other facts (11)
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 | Matrix | [1] |
| Rdf:type | Feature Matrix | [2] |
| Rdf:type | Variable | [4] |
| Rdf:type | Test Feature Matrix | [5] |
| Rdf:type | Test Features | [6] |
| Derived From | Vectorizer | [3] |
| Derived From | X Test | [3] |
| Shape | Test Feature Matrix | [3] |
| Computed by | Vectorizer | [4] |
| Input to | Model | [4] |
| Used by | Voting Model | [5] |
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 (6)
ctx:claims/beam/1680fd31-ef75-4b8f-b41d-f9807171b358- full textbeam-chunktext/plain1 KB
doc:beam/1680fd31-ef75-4b8f-b41d-f9807171b358Show excerpt
grid_search.fit(X_train_tfidf, y_train) # Best model best_model = grid_search.best_estimator_ # Make predictions predictions = best_model.predict(X_test_tfidf) # Calculate the recall score recall = recall_score(y_test, predictions) print…
ctx:claims/beam/82542fdb-a2be-4da5-9db6-63ce30f861b6- full textbeam-chunktext/plain1 KB
doc:beam/82542fdb-a2be-4da5-9db6-63ce30f861b6Show excerpt
predictions = model.predict(X_test_tfidf) # Calculate the recall score recall = recall_score(y_test, predictions) print(f'Recall score: {recall:.3f}') # Print classification report and confusion matrix print(classification_report(y_test, …
ctx:claims/beam/df11b3fa-ca37-4721-9ab9-c56d1bc73bf0- full textbeam-chunktext/plain1 KB
doc:beam/df11b3fa-ca37-4721-9ab9-c56d1bc73bf0Show excerpt
# Define a threshold to determine sparsity threshold = 10 # Example threshold return len(document.split()) < threshold df['is_sparse'] = df['text'].apply(is_sparse) # Separate sparse and dense documents sparse_df = df[df['is_…
ctx:claims/beam/d3954c6e-57e2-4e9f-b834-ff3def382c8d- full textbeam-chunktext/plain1 KB
doc:beam/d3954c6e-57e2-4e9f-b834-ff3def382c8dShow excerpt
# Identify sparse and dense documents def is_sparse(document): # Define a threshold to determine sparsity threshold = 10 # Example threshold return len(document.split()) < threshold df['is_sparse'] = df['text'].apply(is_sparse…
ctx:claims/beam/57063f8a-831c-4360-b1ef-31c5a88beadd- full textbeam-chunktext/plain1 KB
doc:beam/57063f8a-831c-4360-b1ef-31c5a88beaddShow excerpt
model1.fit(X_train_tfidf, y_train) model2.fit(X_train_tfidf, y_train) # Combine models using voting classifier voting_model = VotingClassifier(estimators=[('lr', model1), ('rf', model2)], voting='soft') voting_model.fit(X_train_tfidf, y_tr…
ctx:claims/beam/4b350633-6322-4093-993a-e7268aabef00- full textbeam-chunktext/plain1 KB
doc:beam/4b350633-6322-4093-993a-e7268aabef00Show excerpt
# Train the model model.fit(X_train_tfidf, y_train) # Make predictions predictions = model.predict(X_test_tfidf) # Calculate the recall score recall = recall_score(y_test, predictions) print(f'Recall score: {recall:.3f}') # Print classif…
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