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An R-Based Landscape Validation of a Competing Risk Model
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Understanding overfitting in random forest for probability estimation: a visualization and simulation study.

Lasai Barreñada1,2, Paula Dhiman3, Dirk Timmerman1,4

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Summary
This summary is machine-generated.

Random forests create probability "spikes" for clinical risk prediction, leading to high training AUCs. However, these peaks do not significantly harm test data performance, though fully grown trees may not be optimal for probability estimation.

Keywords:
Prediction modelingRandom ForestRisk estimation

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Area of Science:

  • Machine Learning
  • Biostatistics
  • Clinical Risk Prediction

Background:

  • Random forests are popular for clinical risk prediction.
  • High training AUCs near 1 were observed in a case study, suggesting potential overfitting.
  • The study investigates random forest behavior in probability estimation.

Purpose of the Study:

  • To understand random forest behavior for probability estimation.
  • To visualize data space in real-world case studies and a simulation study.
  • To assess the impact of model parameters on performance.

Main Methods:

  • Visualized risk estimates using heatmaps in 2D subspaces for case studies.
  • Conducted a simulation study with 48 logistic data-generating mechanisms (DGMs).
  • Varied predictor distributions, number/correlation of predictors, true AUC, and predictor strength; trained random forest models using the ranger R package.

Main Results:

  • Visualizations revealed "spikes of probability" around training data events.
  • Median training AUCs were high (0.97-1) unless specific predictor/node size conditions were met.
  • Discrimination loss was moderate (median 0.025), with test AUCs influenced by events per variable, node size, and predictor type.

Conclusions:

  • Random forests learn local probability peaks, often resulting in near-perfect training AUCs.
  • These peaks generally do not strongly affect test data AUCs.
  • For probability estimation, results challenge the recommendation of using fully grown trees in random forest models.