recall_score

total_duration += timer.duration total_throughput += num_queries / timer.duration latencies.append(timer.duration) # Assuming results is a binary array indicating relevance precision = precision_scor…

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score def evaluate(y_true, y_pred): acc = accuracy_score(y_true, y_pred) prec = precision_score(y_true, y_pred, average='weighted') …

Here's an example of how you might set up the grid search and logging: ```python from sklearn.model_selection import train_test_split from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score import logging # Exa…

- **Try Different Models**: Experiment with other models like SVM, RandomForest, or GradientBoosting. - **Feature Engineering**: Consider additional feature engineering techniques to improve model performance. - **Class Imbalance**: If your…

SVMs can be effective, especially with the right kernel and parameter tuning. ### 4. **Decision Tree Classifier** Decision Trees are simple yet effective for certain types of data and can be used as a baseline. ### 5. **Naive Bayes Classi…

By trying multiple models and performing hyperparameter tuning, you can identify the best model for your dataset and improve the recall score. This approach allows you to leverage the strengths of different algorithms and find the one that …

Decision Trees are relatively fast to train and can handle sparse data well. They are particularly useful as a baseline model. ### 4. **Linear Support Vector Machine (SVM)** A linear SVM can be quite fast to train, especially with sparse d…

recall = recall_score(y_test, predictions) print(f'{name} Recall score: {recall:.3f}') print(classification_report(y_test, predictions)) print(confusion_matrix(y_test, predictions)) print('-' * 50) ``` ### Explanat…

# 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…

### Step 2: Modify the Code to Use BM25 Here's an example of how you can integrate BM25 into your proof of concept: ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import recall_scor…

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'…

By following these steps, you can ensure that your evaluation pipeline is robust, transparent, and continuously improving. [Turn 9436] User: hmm, can I integrate these logging improvements into my existing CI/CD pipeline? [Turn 9437] Assi…

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 …