Explore a brief overview of this analysis, including performance metrics, evaluation results, and key characteristics of the model. Visualizations below provide insights at a glance.
This section presents a demographic breakdown of the dataset used to train the model, together with visualisations of the class balance, error rate and maximum equalised odds difference. The goal is to highlight whether model performance varies across demographic groups and to surface potential source of bias.
Shows the distribution of positive and negative outcomes within each group. This provides context on data availability and outcome representation. Strong imbalance or small sample sizes can affect the reliability of performance and fairness metrics.
Shows model errors across demographic pairs (e.g. Male 18–25, Female 26–35) using two complementary measures:
Lower values indicate better performance for a group, while large differences between groups may suggest uneven performance or potential fairness concerns.
Summarises the maximum difference in FPR and FNR between groups. Values close to zero suggest more consistent model behaviour, while higher values indicate greater disparity in error patterns across demographic groups.