Grid Search is a hyperparameter tuning technique that involves defining a grid of hyperparameter values and searching exhaustively through all possible combinations to find the best set of hyperparameters for a machine learning model. It’s a simple but computationally expensive method.
1. Grid Search Process
- Define Hyperparameter Grid: Specify a range of values for each hyperparameter you want to tune.
- Create a Parameter Grid: Generate all possible combinations of hyperparameter values.
- Train-Test Model for Each Combination: For each combination, train and evaluate the model using cross-validation.
- Select the Best Combination: Identify the combination of hyperparameters that yields the best performance.
2. Advantages:
- Exhaustive Search: Grid search systematically explores all possible combinations in the defined hyperparameter space, leaving no stone unturned.
- Simple Implementation: Implementation is straightforward and easy to understand, making it accessible for users with varying levels of expertise.
3. Disadvantages:
- Computational Cost: Grid search…
