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Found 8 papers, 5 papers with code
Large-scale benchmark study of survival prediction methods using multi-omics data
The Kaplan-Meier estimate and a Cox model using only clinical variables were used as reference methods.
Learning Multiple Defaults for Machine Learning Algorithms
The performance of modern machine learning methods highly depends on their hyperparameter configurations.
Automatic Exploration of Machine Learning Experiments on OpenML
Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can help to improve automatic hyperparameter tuning procedures.
Hyperparameters and Tuning Strategies for Random Forest
In a benchmark study on several datasets, we compare the prediction performance and runtime of tuneRanger with other tuning implementations in R and RF with default hyperparameters.
Tunability: Importance of Hyperparameters of Machine Learning Algorithms
Firstly, we formalize the problem of tuning from a statistical point of view, define data-based defaults and suggest general measures quantifying the tunability of hyperparameters of algorithms.
To tune or not to tune the number of trees in random forest?
The number of trees T in the random forest (RF) algorithm for supervised learning has to be set by the user.
Multilabel Classification with R Package mlr
We implemented several multilabel classification algorithms in the machine learning package mlr.
mlr Tutorial
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.
