The hyperparameter optimization (HPO) is usually a high-dimensional black box optimization problem and often faces expensive function evaluations. Besides, the task of HPO has gained great attention ...

In data science, tuning hyperparameters is akin to fine-tuning an instrument for the best sound. Hyperparameters are the settings of an algorithm that need to be specified before training a model ...

This surrogate model is much easier to optimize than the original function. ... We all know the importance of hyperparameter optimization while training a machine learning model.

Purpose: Perform Hyperparameter Optimization using SMAC with CFPNet-M as the surrogate model. Details: Initializes a hyperparameter configuration space, establishes a particular scenario for ...

Most of the approaches for tuning hyperparameters fall into Sequential Model- based Global Optimization (SMBO). These approaches use a surrogate function to approximate the true blackbox function.

In machine learning, algorithms harness the power to unearth hidden insights and predictions from within data. Central to the effectiveness of these algorithms are hyperparameters, which can be ...

Surrogate modeling is not a magic bullet for vehicle aerodynamics optimization and has some limitations and challenges. Choosing the appropriate type of surrogate model, such as polynomial, neural ...

Machine learning techniques have achieved remarkable development in recent years. However, the performance of many machine learning models usually involves careful tuning of hyperparameters. The ...