Model Optimization
Model optimization is the process of improving a machine learning model's performance by fine-tuning its parameters and structure. This involves adjusting various settings, such as learning rates and regularization techniques, to enhance the model's ability to make accurate predictions on new data. The goal is to minimize errors and maximize efficiency.
Techniques for model optimization include grid search, random search, and Bayesian optimization. These methods help identify the best combination of parameters by systematically exploring different options. Effective optimization can lead to better generalization, allowing the model to perform well on unseen data while avoiding overfitting.