Break your ML model into smaller sub-models, each solving a specific part of the problem, and combine their predictions. For example, for a loan approval system, instead of a single complex model, we can build one for credit history, another for income trends, and a third for spending patterns. This modular approach keeps each component simple while boosting overall performance.

In machine learning, I balance complexity and predictive performance using several strategies. Regularization techniques like L1 (Lasso) and L2 prevent overfitting while maintaining interpretability. Feature selection helps focus on impactful variables, simplifying the model and reducing noise. I use k-fold cross-validation to ensure the model generalizes well to unseen data. Hyperparameter tuning via grid search or Bayesian optimization optimizes performance without unnecessary complexity. When required, I implement ensemble methods like bagging or boosting to improve accuracy while avoiding overfitting. Monitoring key metrics like precision, recall, and F1 score ensures the model aligns with project objectives, efficient results.

To balance model complexity with predictive performance in Machine Learning, apply these best practices: Simplify Features: Use feature selection or dimensionality reduction to eliminate irrelevant data. Regularize Models: Apply techniques like L1/L2 regularization to prevent overfitting. Choose the Right Algorithm: Match model complexity to the dataset size and nature. Validate Effectively: Use cross-validation to assess performance across different subsets. Monitor Metrics: Focus on performance metrics like precision, recall, or RMSE to ensure predictions meet objectives. By strategically managing complexity, you can achieve robust, accurate models without sacrificing performance.

To balance model complexity and performance in machine learning: Regularization: Use L1 (Lasso) or L2 (Ridge) regularization to penalize overly complex models and reduce overfitting. Feature Selection: Choose only the most relevant features to simplify the model and reduce noise. Cross-Validation: Use k-fold cross-validation to assess model generalization and prevent overfitting to the training data. Hyperparameter Tuning: Optimize hyperparameters with grid search or random search to find the best model configuration. Ensemble Methods: Use bagging (e.g., Random Forest) or boosting (e.g., XGBoost) to combine multiple models for better performance and lower variance.

For offline mode training on top of these recommendations The domain expert can come to help. Ask them to use heuristics for the features which they feel is important and let them predict the outcome without ML. On the data used for training, check the distribution of training and test data on offline mode and keep these versions. Once model in production for online. Using MLOps techniques to monitor the model performance for drift. Make sure to capture both the data and prediction. Check both the performance of the model in production and production data to the previously stored for training. Investigate the reason for drift, have it taken down, re-trained either incrementally or full offline to be deployed to production again.

When looking at bias vs variance, models that have better predictions also tend to have higher rates of overfitting. Ways to reduce this over fitting while having high predictive quality could be a mixture of regularization / feature selection using elastic net and fine tuning the alpha and lambda values using cross validation. You could also reduce collonearity through principal component analysis.