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  2. Accuracy paradox - Wikipedia

    en.wikipedia.org/wiki/Accuracy_paradox

    Even though the accuracy is ⁠ 10 + 999000 / 1000000 ⁠ ≈ 99.9%, 990 out of the 1000 positive predictions are incorrect. The precision of ⁠ 10 / 10 + 990 ⁠ = 1% reveals its poor performance. As the classes are so unbalanced, a better metric is the F1 score = ⁠ 2 × 0.01 × 1 / 0.01 + 1 ⁠ ≈ 2% (the recall being ⁠ 10 + 0 / 10 ...

  3. Fixed-odds betting - Wikipedia

    en.wikipedia.org/wiki/Fixed-odds_betting

    In making a bet where the expected value is positive, one is said to be getting "the best of it". For example, if one were to bet $1 at 10 to 1 odds (one could win $10) on the outcome of a coin flip, one would be getting "the best of it" and should always make the bet (assuming a rational and risk-neutral attitude with linear utility curves and have no preferences implying loss aversion or the ...

  4. Positive and negative predictive values - Wikipedia

    en.wikipedia.org/wiki/Positive_and_negative...

    The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.

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  6. 50 Predictions For The Next Thirty Years People Think Have A ...

    www.aol.com/people-share-70-things-believe...

    "The future is now, old man." The post 50 Predictions For The Next Thirty Years People Think Have A 99% Chance Of Becoming A Reality first appeared on Bored Panda.

  7. Base rate fallacy - Wikipedia

    en.wikipedia.org/wiki/Base_rate_fallacy

    An example of the base rate fallacy is the false positive paradox (also known as accuracy paradox). This paradox describes situations where there are more false positive test results than true positives (this means the classifier has a low precision). For example, if a facial recognition camera can identify wanted criminals 99% accurately, but ...

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