ROC Curve with AUC in Python, R, Julia and JavaScript

Source code in 15 libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts.

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.

Renders

• Full-resolution render, light theme
• Full-resolution render, dark theme

Preview render from the plotnine implementation.

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)
• ROC Curve with AUC in ggplot2 (R)
• ROC Curve with AUC in Makie.jl (Julia)
• ROC Curve with AUC in Chart.js (JavaScript)
• ROC Curve with AUC in D3.js (JavaScript)
• ROC Curve with AUC in Apache ECharts (JavaScript)
• ROC Curve with AUC in Highcharts (JavaScript)
• ROC Curve with AUC in MUI X Charts (JavaScript)