Overfitting in prediction models – Is it a problem only in high dimensions?
| Autor: | Jyothi Subramanian, Richard Simon |
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| Rok vydání: | 2013 |
| Předmět: |
Training set
business.industry Small number General Medicine Predictor variables Models Theoretical Overfitting Prognosis computer.software_genre Machine learning Predictive Value of Tests Sample Size Test set Diagnosis Evaluation methods Humans Pharmacology (medical) Data mining Artificial intelligence Precision Medicine business computer Classifier (UML) Predictive modelling Mathematics |
| Zdroj: | Contemporary Clinical Trials. 36:636-641 |
| ISSN: | 1551-7144 |
| Popis: | The growing recognition that human diseases are molecularly heterogeneous has stimulated interest in the development of prognostic and predictive classifiers for patient selection and stratification. In the process of classifier development, it has been repeatedly emphasized that in situations where the number of candidate predictor variables is much larger than the number of observations, the apparent (training set, resubstitution) accuracy of the classifiers can be highly optimistically biased and hence, classification accuracy should be reported based on evaluation of the classifier on a separate test set or using complete cross-validation. Such evaluation methods have however not been the norm in the case of low-dimensional, p |
| Databáze: | OpenAIRE |
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