##  [Accuracy](/accuracy-0) 

 Definition

The proportion of a system's outputs or predictions that exactly match the true or intended values; in computational evaluation, usually reported as the fraction of correct results over all cases.

 

 

 

 

 

 





## Principle

Principle

Measure overall correctness by counting true matches against a known ground truth; treats all errors equally and aggregates across classes or outcomes.

 

 

 

 

 





## Demonstration

Demonstration

In a binary classifier tested on 100 labeled examples where 82 predictions equal their labels, accuracy = 82/100 = 0.82.

 

 

 

 

## Misapplication

Misapplication

Using accuracy on a heavily imbalanced dataset to claim good performance when a trivial classifier that always predicts the majority class attains high accuracy while failing on the minority class.

 

 

 

 

 





## Consequence

Consequence

When used appropriately with balanced or appropriately weighted datasets, accuracy provides a single-number summary of correctness that is easy to interpret and compare.

 

 

 

 

## Reversal

Reversal

Focusing on per-class recall or precision instead of overall accuracy highlights class-specific performance and can reveal poor behavior hidden by a high accuracy number.

 

 

 

 

 





## Boundary

Boundary

Applies to problems with a well-defined ground truth and discrete evaluable outputs; excludes continuous error metrics (e.g., mean squared error) and contexts where class imbalance makes raw proportion misleading without weighting or complementary metrics.

 

 

 

 

 





## Semantic Tension

Semantic Tension

Often confounded with 'precision' and 'recall' in information-retrieval contexts; accuracy is an aggregate correctness measure, whereas precision measures positive predictive value and recall measures true-positive rate.

 

 

 

 

 





## Synthesis

Synthesis

Accuracy is the aggregate fraction of correct outputs relative to a defined ground truth; it is simple and informative for balanced, discrete tasks but must be complemented by class-aware or continuous metrics when distributional or severity differences matter.