Is accuracy a proper scoring rule?
The classification accuracy score (percent classified correctly), a single-threshold scoring rule which is zero or one depending on whether the predicted probability is on the appropriate side of 0.5, is a proper scoring rule but not a strictly proper scoring rule because it is optimized (in expectation) not only by …
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How is classification accuracy an improper scoring rule?
Classification accuracy is a discontinuous scoring rule. It implicitly or explicitly uses thresholds for probabilities, and moving a prediction from 0.0001 below the threshold to 0.0001 above the thresholds results in a full accuracy change of 1/N. Classification accuracy is also an improper scoring rule.
What does proper scoring mean in machine learning?
Note that “proper” here has a precisely defined meaning – there are improper scoring rulesas well as proper scoring rulesand finally strictly proper scoring rules. Scoring rulesas such are loss functions of predictive densities and outcomes.
Is it possible to get 90 percent classification accuracy?
Achieving 90 percent classification accuracy, or even 99 percent classification accuracy, may be trivial on an imbalanced classification problem.
What should be the accuracy of classification predictive modeling?
Typically, classification predictive modeling is practiced with small datasets where the class distribution is equal or very close to equal. Therefore, most practitioners develop an intuition that large accuracy score (or conversely small error rate scores) are good, and values above 90 percent are great.