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  2. F-score - Wikipedia

    en.wikipedia.org/wiki/F-score

    Precision and recall. In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly ...

  3. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    A precision-recall curve plots precision as a function of recall; usually precision will decrease as the recall increases. Alternatively, values for one measure can be compared for a fixed level at the other measure (e.g. precision at a recall level of 0.75) or both are combined into a single measure.

  4. Evaluation of binary classifiers - Wikipedia

    en.wikipedia.org/wiki/Evaluation_of_binary...

    Commonly used metrics include the notions of precision and recall. In this context, precision is defined as the fraction of documents correctly retrieved compared to the documents retrieved (true positives divided by true positives plus false positives), using a set of ground truth relevant results selected by humans. Recall is defined as the ...

  5. Evaluation measures (information retrieval) - Wikipedia

    en.wikipedia.org/wiki/Evaluation_measures...

    By computing a precision and recall at every position in the ranked sequence of documents, one can plot a precision-recall curve, plotting precision () as a function of recall . Average precision computes the average value of p ( r ) {\displaystyle p(r)} over the interval from r = 0 {\displaystyle r=0} to r = 1 {\displaystyle r=1} : [ 7 ]

  6. 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 ...

  7. Formula 1: Lando Norris wins Abu Dhabi Grand Prix to clinch ...

    www.aol.com/sports/formula-1-lando-norris-wins...

    Lando Norris won the Formula 1 Abu Dhabi Grand Prix from pole on Sunday. (Photo by Mark Thompson/Getty Images) ... Race results. 1. Lando Norris, McLaren. 2. Carlos Sainz, Ferrari. 3. Charles ...

  8. P4-metric - Wikipedia

    en.wikipedia.org/wiki/P4-metric

    The main assumption behind this metric is, that a properly designed binary classifier should give the results for which all the probabilities mentioned above are close to 1. P 4 is designed the way that P 4 = 1 {\displaystyle \mathrm {P} _{4}=1} requires all the probabilities being equal 1.

  9. Norris defies orders to help Piastri and Verstappen loses the ...

    www.aol.com/norris-hands-piastri-win-qatar...

    Lando Norris ignored team orders and handed his McLaren teammate Oscar Piastri the sprint race in Qatar on Saturday, while Formula 1 champion Max Verstappen was stripped of the pole position.