Lesson 49: ML Best Practices

ML Best Practices Overview

Following best practices in machine learning helps ensure your models are robust, reliable, and production-ready. This lesson covers essential practices for building, evaluating, and deploying ML models effectively.

Best practices span the entire ML lifecycle: from data collection and preprocessing to model training, evaluation, and deployment. Adhering to these practices can significantly improve model performance and reliability.

Best Practice: Proper Train/Test Split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# Always split data before preprocessing to avoid data leakage
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Fit scaler ONLY on training data
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)  # Transform test using training statistics

print("Best practice: No data leakage!")

Data Best Practices

Quality data is fundamental to successful ML:

Data Splitting: Use train/validation/test splits (e.g., 60/20/20) with stratification for classification
Data Leakage: Fit preprocessing (scaling, encoding) only on training data, then transform test data
Handling Missing Values: Understand why data is missing; use appropriate imputation strategies
Feature Engineering: Create domain-specific features, but avoid overfitting to training data
Data Validation: Validate data quality, check for outliers, and ensure consistency

Cross-Validation for Robust Evaluation
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.ensemble import RandomForestClassifier

# Use stratified k-fold cross-validation for classification
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
model = RandomForestClassifier(n_estimators=100)

scores = cross_val_score(model, X_train, y_train, cv=cv, scoring='accuracy')
print(f"Mean CV accuracy: {scores.mean():.4f} (+/- {scores.std() * 2:.4f})")

# More reliable than single train/test split!

Model Training Best Practices

Train models effectively:

Start Simple: Begin with baseline models before complex architectures
Hyperparameter Tuning: Use grid search or random search with cross-validation
Regularization: Use L1/L2 regularization, dropout to prevent overfitting
Early Stopping: Monitor validation loss and stop training when it stops improving
Ensemble Methods: Combine multiple models for better performance

💡 Validation Set Importance

Always use a separate validation set (not test set) for hyperparameter tuning and model selection. The test set should only be used for final evaluation to get an unbiased estimate of model performance!

Evaluation and Monitoring

Proper evaluation ensures reliable models:

Choose Appropriate Metrics: Use metrics that align with business goals (e.g., precision/recall for imbalanced classes)
Monitor Overfitting: Compare training vs. validation performance; large gap indicates overfitting
Production Monitoring: Track model performance, data drift, and prediction distributions in production
Documentation: Document model assumptions, limitations, and performance characteristics

Deployment Best Practices

When deploying models:

Model Versioning: Track model versions and enable rollback capabilities
Input Validation: Validate inputs at API level to catch errors early
Error Handling: Handle edge cases gracefully with appropriate error messages
Performance Monitoring: Track latency, throughput, and resource usage
Gradual Rollout: Use canary deployments to test new models with subset of traffic

Exercise: Implement Best Practices

In the exercise on the right, you'll implement several best practices: proper data splitting, preprocessing without data leakage, cross-validation, and model evaluation. This exercise reinforces key practices for building reliable ML models.

This hands-on exercise will help you understand how to apply best practices throughout the ML workflow.

ML Best Practices