J. Verbakel

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom J. Verbakel trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Ewout W. Steyerberg
  2. Anne-Laure Boulesteix
  3. B. Van calster

Papers from J. Verbakel's perspective

Ordered by how strongly the researchers J. Verbakel trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Do we need hundreds of classifiers to solve real world classification problems?
  2. Clinical Prediction Models
  3. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests (British medical journal)
  4. A Plea for Neutral Comparison Studies in Computational Sciences
  5. Ten Simple Rules for Reducing Overoptimistic Reporting in Methodological Computational Research
  6. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. (Journal of Clinical Epidemiology)
  7. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View
  8. The elements of statistical learning: data mining, inference and prediction
  9. A calibration hierarchy for risk models was defined: from utopia to empirical data. (Journal of Clinical Epidemiology)
  10. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints (BMC Medical Research Methodology)

Their papers