Anne-Laure Boulesteix

Understanding overfitting in random forest for probability estimation: a visualization and simulation study

Random forests have become popular for clinical risk prediction modelling. In a case study on predicting ovarian malignancy, we observed training c-statistics close to 1. Although this suggests...

Hyperparameters and Tuning Strategies for Random Forest

The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are drawn with or...

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several types such as...

Large-scale benchmark study of survival prediction methods using multi-omics data

Multi-omics data, that is, datasets containing different types of high-dimensional molecular variables (often in addition to classical clinical variables), are increasingly generated for the...

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. It is controversial whether T should simply be set to the largest computationally...

A Plea for Neutral Comparison Studies in Computational Sciences

In a context where most published articles are devoted to the development of "new methods", comparison studies are generally appreciated by readers but surprisingly given poor consideration by...

Position: Why We Must Rethink Empirical Research in Machine Learning

Complexity Selection with Cross-validation for Lasso and Sparse Partial Least Squares Using High-Dimensional Data

On the Simultaneous Analysis of Clinical and Omics Data: A Comparison of Globalboosttest and Pre-validation Techniques

Overcoming Barriers to Computational Reproducibility

Computational reproducibility, the possibility for independent researchers to exactly reproduce published empirical results, is fundamental to science. Despite its importance, the proportion of...

Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications

Adequately generating and evaluating prediction models based on supervised machine learning (ML) is often challenging, especially for less experienced users in applied research areas. Special...

Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study

When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial tool. Luckily,...

Position: Why We Must Rethink Empirical Research in Machine Learning

We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress...

Evaluating machine learning models in non-standard settings: An overview and new findings

Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, particularly those...