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Found 6 papers, 2 papers with code
A Probabilistic Neural Twin for Treatment Planning in Peripheral Pulmonary Artery Stenosis
The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning.
Multi-Objective Hyperparameter Optimization -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance.
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data
Results show that GAM and RF (mean AUROC estimates 0. 708 and 0. 699) outperform all other methods in predictive accuracy.
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model.
mlr Tutorial
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.
