Grid Search CV is a powerful technique in machine learning for hyperparameter tuning. It systematically explores a predefined set of parameter values to find the optimal combination for a model.

Key Concepts

  • Hyperparameter Tuning: Adjusting parameters that govern the learning process (e.g., C in SVM, n_estimators in Random Forest).
  • Cross-Validation: Evaluates model performance using subsets of data to avoid overfitting.
  • Parameter Grid: A dictionary or list defining the hyperparameters and their candidate values.

How It Works

  1. Define a Grid: Specify hyperparameters and their possible values. 
    param_grid = {'C': [0.1, 1], 'kernel': ['linear', 'rbf']}
  2. Iterate Through Combinations: Train and evaluate the model for each parameter set.
  3. Select Best Model: Choose the combination with the highest accuracy (or other metric).

Applications

  • Model Selection: Compare different algorithms with optimal parameters.
  • Feature Engineering: Tune parameters related to feature scaling or selection.
  • Ensemble Methods: Optimize parameters for boosting or bagging techniques.

Pros & Cons

✅ Pros:

  • Exhaustive search for optimal parameters.
  • Easy to implement.

❌ Cons:

  • Computationally expensive for large grids.
  • May overfit if not combined with cross-validation.

For a deeper dive into cross-validation strategies, check our guide: /model_selection

grid_search_cv

📌 Tip: Use RandomizedSearchCV for efficiency when dealing with high-dimensional parameter spaces!