recall

[Turn 1166] User: I'm working on a proof of concept for testing 2 retrieval tools on 400 documents, and I want to achieve 90% recall, but I'm having trouble with the implementation, can someone help me with this? ```python import numpy as …

retrieved_labels = relevant_labels[retrieved_indices] true_positives = np.sum(retrieved_labels) recall = true_positives / num_relevant return recall # Initialize the recall scores recall_scores = [] for tool in tools: …

- **Average Precision (AP)**: Measure of precision at each relevant document. 4. **Mean Scores**: Calculate the mean of each metric across all queries. ### Additional Metrics 1. **Precision@k**: Precision of the top-k retrieved documen…

# 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}') ```…

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, …

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'…

- **Improved Performance**: Managing the stack manually can be more efficient, especially for large inputs. ### Example Usage When you run the code with a test term, it will expand the synonyms iteratively and print the result. ### Concl…

true_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 …