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  2. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    In a classification task, the precision for a class is the number of true positives (i.e. the number of items correctly labelled as belonging to the positive class) divided by the total number of elements labelled as belonging to the positive class (i.e. the sum of true positives and false positives, which are items incorrectly labelled as belonging to the class).

  3. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    This statistics -related article is a stub. You can help Wikipedia by expanding it.

  4. Confusion matrix - Wikipedia

    en.wikipedia.org/wiki/Confusion_matrix

    The overall accuracy would be 95%, but in more detail the classifier would have a 100% recognition rate (sensitivity) for the cancer class but a 0% recognition rate for the non-cancer class. F1 score is even more unreliable in such cases, and here would yield over 97.4%, whereas informedness removes such bias and yields 0 as the probability of ...

  5. Mean percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_percentage_error

    Because actual rather than absolute values of the forecast errors are used in the formula, positive and negative forecast errors can offset each other; as a result, the formula can be used as a measure of the bias in the forecasts. A disadvantage of this measure is that it is undefined whenever a single actual value is zero.

  6. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    One set, the bootstrap sample, is the data chosen to be "in-the-bag" by sampling with replacement. The out-of-bag set is all data not chosen in the sampling process. When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each ...

  7. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

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  8. Error tolerance (PAC learning) - Wikipedia

    en.wikipedia.org/wiki/Error_Tolerance_(PAC_learning)

    In the classification noise model [6] a noise rate < is introduced. Then, instead of Oracle ( x ) {\displaystyle {\text{Oracle}}(x)} that returns always the correct label of example x {\displaystyle x} , algorithm A {\displaystyle {\mathcal {A}}} can only call a faulty oracle Oracle ( x , η ) {\displaystyle {\text{Oracle}}(x,\eta )} that will ...

  9. Code rate - Wikipedia

    en.wikipedia.org/wiki/Code_rate

    Download as PDF; Printable version; In other projects ... code is the proportion of the data-stream that ... synonymous with net bit rate or useful bit rate exclusive ...