Search Results for author:
Found 12 papers, 6 papers with code
Multi-Objective Hyperparameter Optimization -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers
Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks.
mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R
This contribution reviews the state-of-the-art in spatial and spatiotemporal cross-validation, and introduces the {R} package {mlr3spatiotempcv} as an extension package of the machine-learning framework {mlr3}.
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.
Employing an Adjusted Stability Measure for Multi-Criteria Model Fitting on Data Sets with Similar Features
We propose the approach of tuning the hyperparameters of a predictive model in a multi-criteria fashion with respect to predictive accuracy and feature selection stability.
mlr3proba: An R Package for Machine Learning in Survival Analysis
As machine learning has become increasingly popular over the last few decades, so too has the number of machine learning interfaces for implementing these models.
Feature Selection Methods for Cost-Constrained Classification in Random Forests
Cost-sensitive feature selection describes a feature selection problem, where features raise individual costs for inclusion in a model.
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper.
OpenML Benchmarking Suites
Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks.
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.
OpenML: An R Package to Connect to the Machine Learning Platform OpenML
We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks.
mlr Tutorial
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.
