How Does Grid Search Work
Explore how grid search optimizes machine learning hyperparameters by systematically evaluating every combination. Understand each step from defining parameter ranges to training and selecting the best model, while recognizing the method's thoroughness and computational demands.
We'll cover the following...
- 1. Define the hyperparameters and their possible values
- 2. Create a grid of all possible combinations of hyperparameter values
- 3. Train a model using every possible combination of hyperparameter values
- 4. Evaluate and record the performance of the ML model
- 5. Select the best-performing combination of hyperparameters
- 6. Train the final ML model with selected hyperparameters
The following list is the optimization steps and procedures that the grid search method must follow to find the combination of hyperparameter values that produce the best ML model performance:
Define the hyperparameters and their possible values.
Create a grid of all possible combinations of hyperparameter values.
Train a model using every possible combination of hyperparameter values.
Evaluate and record the performance of the ML model.
Select the best-performing combination of hyperparameters.
Train the final ML model with selected hyperparameters. ...