OverviewCommunityWorkflowResearchCollaborate
Learn Before
  • Leave-One-Out Cross-Validation

    Concept icon
  • K-Fold Cross-Validation

    Concept icon
Relation

K-Fold Cross-Validation vs. Leave-One-Out Cross-Validation

K-Fold CV is usually the method of choice for CV, and functions as a sort of generalization of LOOCV, because:

  1. It is much less computationally expensive than LOOCV.
  2. It minimizes the Bias-Variance Tradeoff.

0

3

Updated 2020-03-07

Contributors are:

CL
Cory Laban
🏆 6
TB
Tirdad Barghi
✔️ 1.5
IY
Iman YeckehZaare
✔️ 1.5

Who are from:

UM
University of Michigan - Ann Arbor
🏆 9

References


  • An Introduction to Statistical Learning with Applications in R

Tags

Data Science

Related
  • Bias–Variance Tradeoff Among Cross-Validation Methods

  • 5≤K≤105 \leq K \leq 105≤K≤10 in K-Fold Cross-Validation

    Concept icon
  • Multiple Testing Resilience in K-Fold Cross-Validation

    Concept icon
  • Identifying Overfitting via Cross-Validation

    Concept icon
  • K-Fold Cross-Validation Bias-Variance Tradeoff

  • sklearn.model_selection.cross_val_score

Learn After
  • K-Fold Cross-Validation is less expensive than LOOCV

    Concept icon
Learn After
  • K-Fold Cross-Validation is less expensive than LOOCV

    Concept icon
Related
  • Bias–Variance Tradeoff Among Cross-Validation Methods

  • 5≤K≤105 \leq K \leq 105≤K≤10 in K-Fold Cross-Validation

    Concept icon
  • Multiple Testing Resilience in K-Fold Cross-Validation

    Concept icon
  • Identifying Overfitting via Cross-Validation

    Concept icon
  • K-Fold Cross-Validation Bias-Variance Tradeoff

  • sklearn.model_selection.cross_val_score

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