Loading content
Technique for automated search of optimal hyperparameters for ML models to improve performance and generalization.
Hyperparameter optimization is a systematic process for automated tuning of model configurations to maximize generalization and performance in ML models. The method includes search strategies (grid, random, Bayesian), validation, model comparison and resource management. It helps improve predictive quality while balancing training cost and overfitting.
Aggregated loss on validation data to assess generalization.
Average prediction time in production mode to assess deployability.
Estimated infrastructure cost per training run as a decision factor.
Grid and random search to select number of trees, depth and split criteria with CV validation.
Bayesian optimization to select learning rate, batch size and regularization under limited GPU budget.
Use of Optuna for Pareto-optimized configurations regarding accuracy and training time.
Define search space, metrics and budget.
Choose an appropriate search strategy (Grid/Random/Bayesian/TPE).
Integrate tracking, run searches and evaluate results.
Validate final selected configurations on a separate test set.