Variables used in formulas

The following common variables are used in formulas of the described metrics:

  •  is the label value for the i-th object (from the input data for training).
  •  is the result of applying the model to the i-th object.
  • is the predicted success probability
  •  is the total number of objects.
  • is the number of classes.
  • is the class of the object for binary classification.

  •  is the weight of the i-th object. It is set in the dataset description in columns with the Weight type (if otherwise is not stated) or in the sample_weight parameter of the Python package. The default is 1 for all objects.
  • , , , , are abbreviations for Positive, True Positive, True Negative, False Positive and False Negative.

    By default, , , , , use weights. For example,

  • is the array of pairs specified in the Pairs description or in the pairs parameter of the Python package.
  • is the number of pairs for the Pairwise metrics.
  • is the value calculated using the resulting model for the winner object for the Pairwise metrics.
  • is the value calculated using the resulting model for the loser object for the Pairwise metrics.
  • is the weight of the (; ) pair for the Pairwise metrics.
  • is the array of object identifiers from the input dataset with a common GroupId. It is used to calculate theGroupwise metrics.
  • is the set of all arrays of identifiers from the input dataset with a common GroupId. It is used to calculate the Groupwise metrics.