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calledWithCalled With(7)ex:accuracy_scoreex:accuracy_scoreex:classification_reportex:classification_reportex:compute_metricsex:confusion_matrixex:confusion_matrixcomparesCompares(4)ex:accuracyex:accuracy_scoreex:compute_metricsex:precision_scoreproducesProduces(3)ex:pipelineex:predictex:predictionusesUses(3)ex:compute_metricsex:first precision_score callex:weighted_scorescontainsVariableContains Variable(2)ex:logging-code-snippetex:pdb-code-snippetbasedOnBased on(1)ex:reportcombinesCombines(1)ex:weighted_scorescomputedFromComputed From(1)ex:accuracyhasParameterHas Parameter(1)ex:precision_scorehasVariableHas Variable(1)ex:code-snippetinstantiatedByInstantiated by(1)ex:predictionsisComputedAfterIs Computed After(1)ex:weighted_scoresparameterParameter(1)log_metricsstoredInStored in(1)ex:initial predictiontakesInputTakes Input(1)ex:accuracy-calculationusedForUsed for(1)ex:retrieved-documentsusedInUsed in(1)ex:npusesComplementUses Complement(1)ex:weighted-scoring-formulaThe 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.
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doc:beam/f008f4ce-021d-4be6-b191-62e598ae1493dataset = pd.read_csv('queries_dataset.csv') # Split the dataset into training and testing sets train_data, test_data = train_test_split(dataset, test_size=0.2) # Train the RAG system (if needed) # ... # Evaluate the system on the test d…
doc:beam/dd6560d5-64d1-4999-ae8b-6d6edb214986y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) logging.debug(f"Model evaluation completed. Accuracy: {accuracy:.4f}") report = classification_report(y_test, y_pred) matrix = confusion_matri…
doc:beam/2cabe7c4-5c3a-4acb-96c0-d14c7053114clogging.debug("Starting model evaluation...") y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) logging.debug(f"Model evaluation completed. Accuracy: {accuracy:.4f}") ``` #### 2. **Use Debugging Tools** Next, use `p…
doc:beam/4cc521bd-2791-4334-88dc-f5e3519e2d922. **Split the Dataset**: Divide the dataset into training and testing sets. 3. **Evaluate Precision and Recall**: Use precision and recall to evaluate the relevance of the retrieved documents. 4. **User Feedback**: Optionally, collect user…
doc:beam/575c6f15-a6fa-439f-9d3d-ef28e0854e79best_score = grid_search.best_score_ print(f"Best parameters: {best_params}") print(f"Best cross-validation accuracy: {best_score:.4f}") # Re-fit with best parameters pipeline.set_params(**best_params) pipeline.fit(X_train, y_train) # Fi…
doc:beam/424105bf-6157-4437-85d8-d148da0857d2X = data.drop(columns=['relevance_score']) y = data['relevance_score'] # Split data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Define preprocessing steps prep…
doc:beam/ca2653b8-c25f-4a54-bdfa-ff6ea71f5472true_vector = [doc in ground_truth_documents for doc in retrieved_documents] pred_vector = [True] * len(retrieved_documents) y_true.extend(true_vector) y_pred.extend(pred_vector) # Calculate precision and recall precision …
doc:beam/d8afae17-1d41-41a0-98bd-510a77330309X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y) # Standardize the data scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Define the …
doc:beam/8511e19b-1795-4c4b-b967-d8360ac84264X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_classes=2, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state= 42) # Step 3: Implement Automated Testing def …
doc:beam/ba4ebe5f-d07c-449d-a419-da14a14caa93from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # Load dataset and split into training and testing sets X_train, X_test, y_train, y_test = …
doc:beam/8951974a-470b-4a56-8030-ad3ac43f8c5ffrom sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # Assuming I have a DataFrame with document types and features df = pd.read_csv('documents.csv') # Split data into training and testing sets X_…
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