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  2. Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_determination

    Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

  3. Nash–Sutcliffe model efficiency coefficient - Wikipedia

    en.wikipedia.org/wiki/Nash–Sutcliffe_model...

    The Nash–Sutcliffe coefficient masks important behaviors that if re-cast can aid in the interpretation of the different sources of model behavior in terms of bias, random, and other components. [11]

  4. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    To calculate the recall for a given class, we divide the number of true positives by the prevalence of this class (number of times that the class occurs in the data sample). The class-wise precision and recall values can then be combined into an overall multi-class evaluation score, e.g., using the macro F1 metric. [21]

  5. Mean absolute percentage error - Wikipedia

    en.wikipedia.org/wiki/Mean_absolute_percentage_error

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  6. Phi coefficient - Wikipedia

    en.wikipedia.org/wiki/Phi_coefficient

    In statistics, the phi coefficient (or mean square contingency coefficient and denoted by φ or r φ) is a measure of association for two binary variables.. In machine learning, it is known as the Matthews correlation coefficient (MCC) and used as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975.

  7. Confusion matrix - Wikipedia

    en.wikipedia.org/wiki/Confusion_matrix

    According to Davide Chicco and Giuseppe Jurman, the most informative metric to evaluate a confusion matrix is the Matthews correlation coefficient (MCC). [ 11 ] Other metrics can be included in a confusion matrix, each of them having their significance and use.

  8. Receiver operating characteristic - Wikipedia

    en.wikipedia.org/wiki/Receiver_operating...

    A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).

  9. Pseudo-R-squared - Wikipedia

    en.wikipedia.org/wiki/Pseudo-R-squared

    The last value listed, labelled “r2CU” is the pseudo-r-squared by Nagelkerke and is the same as the pseudo-r-squared by Cragg and Uhler. Pseudo-R-squared values are used when the outcome variable is nominal or ordinal such that the coefficient of determination R 2 cannot be applied as a measure for goodness of fit and when a likelihood ...