ROCAUC Score
Overview
Calculates the Area Under the Receiver Operating Characteristic Curve (ROC AUC), which is a measure of the model's ability to distinguish between classes. A higher value indicates better performance.
Usage
Manually
# Calculate RocAuc score for all data
rocauc = roc_auc(df, labels, y_true)
print(rocauc)
Using Fairness Object
result = fo.compute(roc_auc)
Results
0.4722222222222222
These results are obtained by using the input data given in the Create Example Data page under Getting Started
Interpretation
ROC AUC score measures the ability of a binary classification model to rank positive instances higher than negative instances across different threshold values. A higher ROC AUC score indicates better model performance in terms of classification accuracy. 1 is considered perfect, and .5 would be a random guess between classes.
Updated 8 months ago