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Confidence bound

As explained in Machine Learning System Design

A statistical limit that bounds an estimated quantity with a stated level of confidence or coverage.

A confidence bound replaces a fragile point estimate with a limit that accounts for uncertainty. In model validation, it helps distinguish apparently strong offline metrics from performance likely to hold online. section 7.4

Coin toss intuition

After 100 fair-coin tosses, the number of heads is a point estimate. Saying that the result should fall between 40 and 60 in 95% of cases gives a confidence interval; 40 is the lower confidence bound. section 7.4

Use confidence bounds when validation decisions depend on uncertain metrics:

  • Treat precision, recall, and similar metrics as distributions rather than single values. section 7.4
  • Use bootstrap resampling for a stronger uncertainty estimate, while accounting for its substantial computational cost. section 7.4

How Machine Learning System Design covers it

How deep it goes
Defines the bound with a coin-toss example and compares point estimates, metric variance, and bootstrap without deriving the statistics.
Examples
Uses a 100-toss coin example, precision and recall distributions, and a 99% lower-bound plot.
What you can do after
Evaluate model performance with uncertainty instead of relying only on a point estimate.
How it connects
Connects confidence bounds with confidence intervals, point estimates, validation sets, precision, recall, and bootstrap.
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.