Logistic Regression Curve Plot 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 logistic regression visualization showing the characteristic S-shaped (sigmoid) probability curve for binary classification. The plot displays data points colored by their binary class, the fitted logistic curve representing predicted probabilities, confidence intervals around the curve, and a dashed decision threshold line at probability 0.5. This visualization is essential for understanding how a logistic model maps continuous input features to class probabilities.

Renders

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

Preview render from the Seaborn implementation.

Implementations

• Logistic Regression Curve Plot in Altair (Python)
• Logistic Regression Curve Plot in Bokeh (Python)
• Logistic Regression Curve Plot in lets-plot (Python)
• Logistic Regression Curve Plot in Matplotlib (Python)
• Logistic Regression Curve Plot in Plotly (Python)
• Logistic Regression Curve Plot in plotnine (Python)
• Logistic Regression Curve Plot in Pygal (Python)
• Logistic Regression Curve Plot in Seaborn (Python)
• Logistic Regression Curve Plot in ggplot2 (R)
• Logistic Regression Curve Plot in Makie.jl (Julia)
• Logistic Regression Curve Plot in Chart.js (JavaScript)
• Logistic Regression Curve Plot in D3.js (JavaScript)
• Logistic Regression Curve Plot in Apache ECharts (JavaScript)
• Logistic Regression Curve Plot in Highcharts (JavaScript)
• Logistic Regression Curve Plot in MUI X Charts (JavaScript)