Explanation

Answer Example

I approach hyperparameter tuning systematically by first identifying key parameters that significantly impact model performance. I start with random search for a broad exploration of the parameter space, followed by grid search or Bayesian optimization for fine-tuning. I also use k-fold cross-validation to ensure robust evaluation. For example, while optimizing an XGBoost model for a recommendation system, I used random search to narrow down the learning rate and max depth, followed by Bayesian optimization to achieve a 10% improvement in precision.