ROC Curve with AUC

A Receiver Operating Characteristic (ROC) curve visualizes the performance of a binary classifier by plotting the True Positive Rate (TPR) against the False Positive Rate (FPR) at various classification thresholds. The Area Under the Curve (AUC) provides a single metric summarizing model performance, where 1.0 indicates perfect classification and 0.5 represents random guessing.

Implementations

• ROC Curve with AUC in Altair (Python)
• ROC Curve with AUC in Bokeh (Python)
• ROC Curve with AUC in lets-plot (Python)
• ROC Curve with AUC in Matplotlib (Python)
• ROC Curve with AUC in Plotly (Python)
• ROC Curve with AUC in plotnine (Python)
• ROC Curve with AUC in Pygal (Python)
• ROC Curve with AUC in Seaborn (Python)