What is Grid Search vs Random Search?

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Grid Search and Random Search are techniques for hyperparameter tuning in machine learning.

🔹 Grid Search:

• Tests all possible combinations of hyperparameters from a predefined set.

• Example: For parameters C = {0.1,1,10} and kernel = {linear, rbf}, Grid Search will try all 6 combinations.

• Advantage: Systematic and guarantees the best result within the chosen grid.

• Disadvantage: Very computationally expensive, especially when many parameters or large ranges are involved.

🔹 Random Search:

• Instead of trying all combinations, it randomly samples values from given parameter ranges.

• Example: If learning rate ∈ [0.0001, 0.1] and batch size ∈ [16, 256], Random Search picks random pairs to evaluate.

• Advantage: Much faster and often finds near-optimal parameters with fewer trials.

• Disadvantage: No guarantee of testing the exact best combination.

👉 Key Difference:

• Grid Search = exhaustive, precise within grid, but slow.

• Random Search = approximate, faster, more practical for large spaces.

Often, practitioners start with Random Search for a broad search, then refine with Grid Search or advanced methods like Bayesian Optimization.

Would you like me to also give a small sklearn code example showing both GridSearchCV and RandomizedSearchCV?

Read More :

What metrics do you use for classification models?

What is cross-validation and why is it used?

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