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 performancePhoto by Scott Webb on Unsplash

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

While many techniques exist, choosing the optimal tuning method depends on factors like:

Model Complexity: More complex models inherently lead to larger search spaces.Data Complexity: The characteristics of the dataset impact tuning difficulty.Familiarity with the Model: Our understanding of the model’s behavior can guide tuning choices and define search spaces.

In this article, I’ll demonstrate right strategies to tune a model using five key tuning methods:

Manual Search,Grid Search,Random Search,Bayesian Optimization, andMetaheuristic Algorithm

in different scenarios, over Convolutional Neural Networks (CNNs) for high-dimensional image data and Kernel Support Vector Machines (SVMs) for simpler tabular data.

Hyperparameter tuning is a technical process to tune the configuration settings of machine learning models, called hyperparameters, before training the model.

Unlike model parameters learned during the training (e.g., weights and bias), hyperparameters are not estimated from data, and most machine leaning models rely on many hyperparameters.

For example, in case of a Convolutional Neural Network (CNN), an input layer shape, convolutional layer settings like the number of filters, filter size, stride, padding, output layer size, and compiler settings like the optimizer, loss function, and evaluation metrics are indeed… Read the full blog for free on Medium.

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Published via Towards AI