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Found 11 papers, 6 papers with code
Position Paper: Rethinking Empirical Research in Machine Learning: Addressing Epistemic and Methodological Challenges of Experimentation
We warn against a common but incomplete understanding of empirical research in machine learning (ML) that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field.
Understanding random forests and overfitting: a visualization and simulation study
For the case studies, risk estimates were visualised using heatmaps in a 2-dimensional subspace.
Evaluating machine learning models in non-standard settings: An overview and new findings
Our findings corroborate the concern that standard resampling methods often yield biased GE estimates in non-standard settings, underscoring the importance of tailored GE estimation.
Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study
Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients.
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.
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.
Essential guidelines for computational method benchmarking
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses.
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.
A U-statistic estimator for the variance of resampling-based error estimators
Thus, it has minimal variance among all unbiased estimators and is asymptotically normally distributed.
