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Are accuracy and precision the same things in regression and classification?

In regression: accuracy is bias, and precision is inverse of variance.

In classification: accuracy is correct prediction over number of samples, and precision is true positives over all predicted positives.

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Accuracy is the overall accuracy of the model. It is the ratio of

(total number of correct predictions) / (total population)


Precision (Positive Prediction Value) on the other hand is the ratio of

(correctly predicted as positive) / (total number of positive predictions)

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