Hyperparameter tuning is critical for the correct functioning of Machine Learning (ML) models. The Grid Search method is a basic tool for hyperparameter optimization.

The Grid Search Method considers several hyperparameter combinations and chooses the one that returns a lower error score. This method is specially useful when there are only a few hyperparameters to optimize, although it is outperformed by other weighted-random search methods when the ML model grows in complexity.

This article introduces the idea of **Grid Search **for hyperparameter tuning. You will learn how a Grid Search works, and how to implement it to optimize the performance of your Machine Learning Method.

Snippets of code are provided to help understanding the implementation. The complete code can be found at this GitHub repository.

Grid Search offers an intuitive way to tune the hyperparameters of ML models, and it is very efficient for low-dimensional spaces.

#machine-learning #data-science #hyperparameter-tuning #python

Grid Search in Python from scratch— Hyperparameter tuning
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