Hyperparameter Tuning in Machine Learning

Two popular techniques

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Introduction

In the world of machine learning, hyperparameter tuning is a crucial step in optimizing model performance. Two popular techniques for this task are GridSearchCV and RandomizedSearchCV, both available in scikit-learn.

In this article, we will learn about the differences between these methods, their advantages and disadvantages, and when to use each one.

Understanding GridSearchCV

GridSearchCV is an exhaustive search method that evaluates all possible combinations of hyperparameters specified in a grid.

Here’s how it works:

1. Define a parameter grid
2. Perform an exhaustive search over all combinations
3. Use cross-validation to assess each combination’s performance
4. Select the best-performing parameter set

Advantages of GridSearchCV

• Guarantees finding the optimal combination within the specified grid
• Produces deterministic results, ensuring reproducibility

Disadvantages of GridSearchCV

• Can be computationally expensive…