Binary Classification

dense_scores = get_embeddings([query]).dot(embeddings.T) combined_scores = 0.5 * sparse_scores + 0.5 * dense_scores return combined_scores # Example usage documents = ["This is a sample document.", "Este es un documento de mues…

vectorizer = TfidfVectorizer() tfidf_matrix = vectorizer.fit_transform(documents) query_vector = vectorizer.transform([query]) similarity_scores = (query_vector * tfidf_matrix.T).toarray() return similarity_scores def h…

- **Early Stopping**: Implement early stopping to halt training if the validation loss does not improve over a certain number of epochs. ### 9. **Model Complexity** - **Simplify the Model**: If the model is too complex, it might over…

- Review the logs and debugging output to identify the root cause of the issue. ### Example Implementation Let's assume you have an evaluation pipeline that uses Scikit-learn for model evaluation. We'll add detailed logging and use `pd…

logging.basicConfig(filename='evaluation_pipeline.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') # Load dataset X, y = np.random.rand(10000, 10), np.random.randint(0, 2, 10000) # Split t…

from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, classification_report, confusion_matrix import logging # Set up logging configuration logg…

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 …