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X Train Tfidf has 15 facts recorded in Dontopedia across 5 references, with 4 live disagreements.
Mostly:rdf:type(5), used by(3), rdfs:label(2)
usedByderivedFromcomputedByshapeOther 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)ex:training_dataex:training_settrainedOnTrained on(2)ex:fit_methodex:modelhasArgumentHas Argument(1)ex:model_trainingtakesInputTakes Input(1)ex:grid_searchusesVariableUses Variable(1)ex:code-blockTimeline 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.
doc:beam/d3954c6e-57e2-4e9f-b834-ff3def382c8d# 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…
doc:beam/df11b3fa-ca37-4721-9ab9-c56d1bc73bf0# 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_…
doc:beam/4b350633-6322-4093-993a-e7268aabef00# 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…
doc:beam/1680fd31-ef75-4b8f-b41d-f9807171b358grid_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…
doc:beam/57063f8a-831c-4360-b1ef-31c5a88beaddmodel1.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…
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