Master Hyperparameter Tuning in Machine Learning

Author(s): Kuriko Iwai

Originally published on Towards AI.

Explore strategies and practical implementation on tuning an ML model to achieve the optimal performance

Hyperparameter tuning is a critical step that significantly impacts model performance in both traditional machine learning and deep learning.

The article discusses five key methods for hyperparameter tuning in machine learning: Manual Search, Grid Search, Random Search, Bayesian Optimization, and Metaheuristic Algorithms. Each method is analyzed for its advantages and limitations in different scenarios, illustrating how they can be applied to complex models like Convolutional Neural Networks (CNNs) and simpler models like Kernel Support Vector Machines (SVMs). The summary includes a comparison of performance metrics such as Mean Absolute Error (MAE) and execution times, emphasizing the importance of selecting an appropriate tuning strategy based on the model’s complexity and computational constraints.

Read the full blog for free on Medium.

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Published via Towards AI