X_test

from 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_…

if re.match(r'\.txt$', file_ext): with open(file_path, 'r', encoding='utf-8') as f: content = f.read() features.append(content) labels.append('text') elif re.match…

# 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_…

# 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…

X = 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…

from 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 = …

model = RandomForestClassifier(n_estimators=100) fine_tuned_model = fine_tune_model(model, X_train, y_train) # Batch processing batch_size = 5000 num_batches = len(X_test) // batch_size for i in range(num_batches): start_idx = i * bat…

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state= 42) # Standardize the data scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Define the model model…

X_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 …

5. **Security**: Ensure that your data and models are secure. Use encryption for sensitive data and follow best practices for securing your deployment environment. 6. **Continuous Integration/Continuous Deployment (CI/CD)**: Implement CI/C…

logging.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…

y_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…

best_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…

scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) # Balanced partitioning # Assuming y_train is imbalanced, we can oversample the minority class minority_class_indices = np.where(y_train ==…

X, 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 …

print(module.get_synonyms('bank', 'geography')) # Output: ['river bank'] ``` ### 4. Machine Learning Models Train machine learning models to predict the most appropriate synonym based on the context of the query. #### Example Implementa…

transformed_outputs = pipeline.transform(X_test) # Evaluate the performance accuracy = (transformed_outputs == y_test).mean() print(f'Transformation accuracy: {accuracy:.2%}') ``` ### Explanation 1. **TextPreprocessor**: Cleans and prepr…

inputs = data['input'] outputs = data['output'] # Split the data into training and testing sets X_train, X_test, y_train, y_test = train_test_split(inputs, outputs, test_size=0.2) # Train the pipeline on the training data pipeline.fit(X_t…