What Grid Search Meaning, Applications & Example
A technique for exhaustively searching a hyperparameter space.
What is Grid Search?
Grid Search is a hyperparameter tuning technique used in machine learning to find the best combination of hyperparameters for a model . It involves exhaustively searching through a predefined set of hyperparameters by evaluating all possible combinations. The goal is to identify the set of hyperparameters that results in the best model performance, typically measured by cross-validation .
How Grid Search Works
- Define Hyperparameters: A grid of hyperparameters is created, with each hyperparameter having a set of possible values (e.g., learning rate , number of layers, batch size).
- Model Training : The model is trained for every combination of hyperparameter values from the grid.
- Evaluation: After training, the model’s performance is evaluated, often using cross-validation, to determine which combination of hyperparameters produces the best results.
Applications of Grid Search
- Model Optimization: Used to optimize machine learning algorithms such as decision trees, support vector machines, and neural networks by finding the best hyperparameters.
- Cross-Validation: Typically used in conjunction with cross-validation techniques to ensure that the model’s performance is generalizable across different subsets of the data.
- Automated Model Tuning: Helps data scientists and machine learning engineers automate the process of hyperparameter selection, improving model accuracy and efficiency.
Example of Grid Search
An example of Grid Search is in tuning a support vector machine (SVM). A grid search can be used to find the optimal values for hyperparameters like the regularization parameter (C) and the kernel type (linear, polynomial, radial basis function). By evaluating the model performance across all combinations, the best-performing SVM configuration can be selected for final use.