In the context of machine learning, grid search is commonly used to find the best hyperparameters for a model. However, you can also use grid search to find optimal parameters or combinations for other types of algorithms or processes that require tuning. Here’s a simple example of using grid search without machine learning:

Let’s say you want to find the best combination of two numbers that gives the maximum result when multiplied. You’ll search over a grid of possible values for these two numbers to find the combination that produces the highest result.

import itertools # Define the possible values for the two numbers possible_values = [1, 2, 3, 4, 5] # Initialize variables to store the best combination and result best_combination = None best_result = float('-inf') # Negative infinity # Iterate over all possible combinations of the two numbers for combo in itertools.product(possible_values, repeat=2): num1, num2 = combo result = num1 * num2 # Update the best combination and result if the current result is higher if result > best_result: best_combination = combo best_result = result print("Best Combination:", best_combination) print("Best Result:", best_result)

In this example, `itertools.product`

generates all possible combinations of the numbers from `possible_values`

. For each combination, the product is calculated, and if it’s greater than the current best result, the best result and combination are updated.

This example demonstrates how grid search can be used to explore and find optimal parameters in a simple non-machine learning context. In practice, grid search is often used with machine learning models to find the best hyperparameters, leading to improved model performance.

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