A Comparison Between Hyperparameter Tuning Techniques — Pros & Cons

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Tuning hyperparameters is a crucial step in optimizing the performance of machine learning models. Here is a list of common ways and techniques to tune hyperparameters:

1. Grid Search

• Define a hyperparameter grid and exhaustively search through all combinations.
• Pros: Simple, easy to implement.
• Cons: Computationally expensive for large search spaces.

2. Random Search

• Randomly sample combinations of hyperparameters.
• Pros: More computationally efficient than grid search.
• Cons: May not find the optimal combination.

3. Bayesian Optimization

• Use probabilistic models to model the objective function and guide the search.
• Pros: Efficient for high-dimensional search spaces, adapts to the shape of the objective function.
• Cons: More complex to implement.

4. Genetic Algorithms

• Mimic the process of natural selection to evolve a population of hyperparameter sets.
• Pros: Can handle both discrete and continuous hyperparameters, good for…