What are the main challenges in model optimization?

What are the main challenges in model optimization?

Model optimization faces several challenges, including overfitting, underfitting, computational cost, and hyperparameter tuning.

Can you explain overfitting and underfitting?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. Underfitting happens when a model is too simple to capture the data's complexity.

What techniques can help mitigate these issues?

Techniques like cross-validation, regularization, and using more complex models can help mitigate overfitting and underfitting.

How does hyperparameter tuning fit into this?

Hyperparameter tuning is crucial as it involves adjusting model parameters to improve performance and prevent overfitting.

Are there specific tools for hyperparameter tuning?

Yes, tools like Grid Search, Random Search, and Bayesian Optimization are commonly used for hyperparameter tuning.