Bootstrap F1 Standard Error Estimation Method

A Bootstrap F1 Standard Error Estimation Method is a resampling-based variance estimation method that estimates F1 score standard errors through repeated sampling with replacement from observed data.

• AKA: Resampling F1 SE Method, Empirical F1 Variance Method, Bootstrap F1 Variance Estimation, Non-Parametric F1 SE Method.
• Context:
  • It can typically generate bootstrap samples by resampling predictions with replacement.
  • It can typically compute F1 scores for each bootstrap replicate.
  • It can typically estimate standard error from bootstrap distribution.
  • It can often avoid distributional assumptions required by Delta-Method F1 Standard Error Estimation Method.
  • It can often handle complex dependency structures in prediction errors.
  • It can often provide confidence intervals through percentile methods.
  • It can range from being a Simple Bootstrap F1 Standard Error Estimation Method to being a Stratified Bootstrap F1 Standard Error Estimation Method, depending on its sampling strategy.
  • It can range from being a Parametric Bootstrap F1 Standard Error Estimation Method to being a Non-Parametric Bootstrap F1 Standard Error Estimation Method, depending on its resampling approach.
  • It can range from being a Basic Bootstrap F1 Standard Error Estimation Method to being a BCa Bootstrap F1 Standard Error Estimation Method, depending on its bias correction.
  • It can range from being a Fast Bootstrap F1 Standard Error Estimation Method to being a Exhaustive Bootstrap F1 Standard Error Estimation Method, depending on its iteration count.
  • ...
• Example(s):
  • Standard Bootstrap F1 SEs, such as:
    • 1000 bootstrap samples, each computing F1, SE = std(F1_boot).
    • B=5000 replicates for high precision SE estimate.
  • Stratified Bootstrap F1 SEs, such as:
    • Maintaining class proportions in each bootstrap sample.
    • Balanced resampling for imbalanced datasets.
  • Confidence Interval Constructions, such as:
    • 95% CI using 2.5th and 97.5th percentiles.
    • BCa intervals correcting for bias and skewness.
  • ...
• Counter-Example(s):
  • Delta-Method F1 Standard Error Estimation Method, which uses analytical derivation.
  • Jackknife F1 SE Method, which uses leave-one-out resampling.
  • Asymptotic F1 SE Method, which assumes large sample normality.
• See: Variance Estimation Method, Bootstrap Method, Resampling Method, F1 Score, Standard Error, Delta-Method F1 Standard Error Estimation Method, BCa Bootstrap F1 Confidence Interval Method, Bootstrap F1 CI Sample Size Guidance Method, Empirical Distribution, Monte Carlo Method, Confidence Interval, Non-Parametric Statistics, Computational Statistics.

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