Kappa Statistic

Definition of Kappa Statistic

Kappa Statistic: Kappa statistic is a statistic used in machine learning and data science that measures the agreement between predicted values and observed values. Kappa statistic is calculated as the average of the absolute agreements (predicting the correct class) minus the average of the absolute disagreements (predicting the wrong class).

How is Kappa Statistic used?

The Kappa statistic is a measure of inter-rater agreement, or consistency between two or more raters. It is used to quantify the amount of agreement that exists between two independent judgments made on the same item. In data science and machine learning, Kappa can be used to quantify the reliability of a model by measuring the consistency between predictions made by different models. For example, if two models trained on different datasets make similar predictions for the same input, then their Kappa value will be higher than if one model makes certain predictions while the other makes completely different ones. The Kappa statistic is also an important tool in comparing results from different versions of a single model, as it can help identify which version of a model is making more accurate predictions. The Kappa statistic is calculated by subtracting the expected probability of agreement from one minus the actual probability of agreement. This means that as long as both models are making correct decisions at least some percentage of the time, their Kappa statistics will be above zero. The higher the Kappa statistic, the greater the degree of agreement between two raters or models. A high Kappa value indicates that both raters or models are agreeing on decisions more often than expected by random chance alone, and thus suggests that their judgments are reliable.

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