Metric Parameters

Below are the parameters used in each of the metric functions

Field	Data Type	Description
df	String (df.to_json())	Pandas DataFrame holding input data and data predicted by a model
y_true	String (y_true.to_json())	Pandas DataFrame holding ground truth values. It should have the same protected_attribute and labels column as df
protected_attribute	String	Column in df signifying the sensitive trait (eg. class, sex, religion etc.)
privileged_group	String	Column in df signifying a group with a historically positive label for their protected attribute (eg. male, abled)
labels	String	Column in df that is the output of the model (eg. isHired?)
positive_label	String, Boolean	Value in labels that is classified as positive
scores	String	Column in df that contains the scores for each row
kwargs	Dictionary	Extra Values (planned to be used for users to specify custom metrics)
epsilon	Number	Trivially small addend used in division operation to avoid division by 0 errors. Default value is 1e-10.
beta	Number	Beta value used in F-Beta metrics explained below. Default value is 1 meaning all F-Beta scores default to F1 scores.