The following is a classwise evaluation index list for each target class, :math:`i`. Weighted averages of evaluation indices are
subsequently computed wherein the weight of a target class is the proportion of the occurrences of the class in the actual population.


.. list-table::
  :header-rows: 1
  :widths: 1, 1, 3

  * - Evaluation Index
    - Type
    - Description
  * - \true_positive_\ *<i>* (**)
    - int
    - Number of samples determined as **positive** for each target class, :math:`i`, correctly (TP).
  * - \false_positive_\ *<i>* (**)
    - int
    - Number of samples determined as **positive** for each target class, :math:`i`, incorrectly (FP).
  * - \true_negative_\ *<i>* (**)
    - int
    - Number of samples determined as **negative** for each target class, :math:`i`, correctly (TN).
  * - \false_negative_\ *<i>* (**)
    - int
    - Number of samples determined as **negative** for each target class, :math:`i`, incorrectly (FN).
  * - \accuracy_\ *<i>*
    - float
    - | Proportion of **true** results for each target class, :math:`i`, in the population as shown below:
      |
      | :math:`\frac{\mbox{TP}_{i} + \mbox{TN}_{i}}{\mbox{TP}_{i} + \mbox{FP}_{i} + \mbox{TN}_{i} + \mbox{FN}_{i}}`
  * - \classification_error_\ *<i>*
    - float
    - | Proportion of **false** results for each target class, :math:`i`, in the population as shown below:
      |
      | :math:`\frac{\mbox{FP}_{i} + \mbox{FN}_{i}}{\mbox{TP}_{i} + \mbox{FP}_{i} + \mbox{TN}_{i} + \mbox{FN}}_{i} = 1 - \mbox{accuracy}_{i}`
  * - \precision_\ *<i>*
    - float
    - | Proportion of the ``true_positive`` of each target class, :math:`i`, against all samples determined as positive as shown below:
      |
      | :math:`\frac{\mbox{TP}_{i}}{\mbox{TP}_{i} + \mbox{FP}_{i}}`
  * - \recall_\ *<i>*
    - float
    - | Proportion of the ``true_positive`` of each target class, :math:`i`, against all the actual positive samples as shown below:
      |
      | :math:`\frac{\mbox{TP}_{i}}{\mbox{TP}_{i} + \mbox{FN}_{i}}`
  * - \specificity_\ *<i>*
    - float
    - | Proportion of the ``true_negative`` of each target class, :math:`i`, against all the actual negative samples as shown below:
      |
      | :math:`\frac{\mbox{TN}_{i}}{\mbox{TN}_{i} + \mbox{FP}_{i}}`
  * - \false_positive_rate_\ *<i>*
    - float
    - | Proportion of the ``false_positive`` of each target class, :math:`i`, against all the actual negative samples as shown below:
      |
      | :math:`\frac{\mbox{FP}_{i}}{\mbox{TN}_{i} + \mbox{FP}_{i}} = 1 - \mbox{specificity}_{i}`
  * - \false_negative_rate_\ *<i>*
    - float
    - | Proportion of the ``false_negative`` of each target class, :math:`i`, against all the actual positive samples as shown below:
      |
      | :math:`\frac{\mbox{FN}_{i}}{\mbox{TP}_{i} + \mbox{FN}_{i}} = 1 - \mbox{recall}_{i}`
  * - \f_measure_\ *<i>*
    - float
    - | Harmonic mean of ``precision`` and ``recall`` of each target class, :math:`i`, as shown below:
      |
      | :math:`\frac{2 \times \mbox{precision}_{i} \times \mbox{recall}_{i}}{\mbox{precision}_{i} + \mbox{recall}_{i}}`
  * - \cf_\ *<i>*\_\ *<j>* (**)
    - int
    - | Confusion matrix values that show the number of actual class, :math:`i`, values predicted as :math:`j`.
      | There are :math:`\mbox{num_target_classes}^{2}` cf index values for every evaluation.

* (**) Weighted average is not computed for this index.