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 calibration curve (reliability diagram) visualizes how well the predicted probabilities of a binary classifier match actual outcomes. By plotting the fraction of positives against mean predicted probability in binned intervals, it reveals whether a model is well-calibrated, overconfident, or underconfident. A perfectly calibrated model follows the diagonal line where predicted probability equals observed frequency.
Preview render from the Plotly implementation.
