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. |
Updated 9 months ago
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