Overfitting, generalization, and MSE in class probability estimation with high‐dimensional data
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Summary
This paper investigates overfitting in the development of regularized class probability estimators and introduces a mean square error decomposition for class probability estimation that helps clarify the relationship between overfitting and prediction accuracy.
- Type
- article
- Published
- 2014-03-01
- Cited by
- 12
- References
- 21
- OpenAlex
- https://openalex.org/W1755363965
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:20790424
Keywords
Overfitting, Estimator, Mean squared error, Statistics, Mathematics
References
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- Statistical Methods in the Atmospheric Sciences
- Regularization Paths for Generalized Linear Models via Coordinate Descent
- Prediction error estimation: a comparison of resampling methods
- Diagnosis of multiple cancer types by shrunken centroids of gene expression
- A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma
- Classification of gene microarrays by penalized logistic regression.
- Modern Applied Statistics with S Fourth edition
- Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction
- Random Forests
- BagBoosting for tumor classification with gene expression data
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- Class probability estimation for medical studies
- DNA methylation-based classification of central nervous system tumours
- Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model
- Identifiability and bias reduction in the skew-probit model for a binary response
- Scalable diagnostic screening of mild cognitive impairment using AI dialogue agent
- Molecular tumor classification using DNA methylome analysis.
- MIFAM-DTI: a drug-target interactions predicting model based on multi-source information fusion and attention mechanism
- Cognitive modeling based on geotagged pictures of urban landscapes using mobile electroencephalogram signals and machine learning models
- Advancing Wheat Single-Nucleotide Polymorphism Data Analysis with Explainable Deep Learning Models
- PartnerMAS: An LLM Hierarchical Multi-Agent Framework for Business Partner Selection on High-Dimensional Features
- Integrated Metabolomic and Transcriptomic Analyses Reveal Alterations in the Serotonergic Synapse Pathway and a Robust Diagnostic Model in Ulcerative Colitis
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