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Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...

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Related Experiment Video

Updated: May 10, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Overfitting in prediction models - is it a problem only in high dimensions?

Jyothi Subramanian1, Richard Simon

  • 1Emmes Corporation, USA.

Contemporary Clinical Trials
|July 2, 2013
PubMed
Summary

Overfitting is a significant risk in developing disease classifiers, even with limited predictor variables. Always validate classifier accuracy using separate test sets or cross-validation to avoid biased results.

Keywords:
ClassifiersClinical trialsOverfittingPatient selectionPrediction accuracy

Related Experiment Videos

Last Updated: May 10, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Biostatistics
  • Bioinformatics
  • Computational Biology

Background:

  • Human diseases exhibit molecular heterogeneity, driving the need for prognostic and predictive classifiers.
  • Traditional classifier development emphasizes validation on separate test sets for high-dimensional data (p>>n).
  • Low-dimensional data (p

Purpose of the Study:

  • To investigate the prevalence and severity of overfitting in low-dimensional data (p
  • To assess the impact of predictor-outcome relationship strength on overfitting in low-dimensional settings.

Main Methods:

  • Simulation studies were conducted to model classifier development with low-dimensional data.
  • The simulations evaluated the extent of optimistic bias in classifier accuracy when using training set evaluation.

Main Results:

  • Overfitting poses a substantial risk even with low-dimensional predictor variables.
  • The risk of overfitting is exacerbated when the relationship between the outcome and predictors is weak.
  • Apparent classifier accuracy on training data can be highly misleading in low-dimensional scenarios.

Conclusions:

  • Separate test set evaluation or complete cross-validation is crucial for accurate classifier assessment, regardless of data dimensionality.
  • Adoption of robust validation methods is recommended to prevent biased prognostic and predictive classifiers in clinical applications.
  • Ensuring reliable patient stratification requires careful validation to mitigate overfitting in classifier development.