The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Seaborn; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Logistic Regression Curve Plot in Python, R, Julia and JavaScript.
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.
""" anyplot.ai
logistic-regression: Logistic Regression Curve Plot
Library: pygal 3.1.0 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-18
"""
import os
import numpy as np
import pygal
from pygal.style import Style
# Theme-adaptive colors
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Okabe-Ito palette
IMPRINT = ("#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477")
# Data - Medical diagnosis based on biomarker level
np.random.seed(42)
n_samples = 150
# Generate biomarker levels (0-100 scale)
biomarker_levels = np.concatenate(
[
np.random.normal(30, 15, n_samples // 2), # Lower levels (mostly negative)
np.random.normal(70, 15, n_samples // 2), # Higher levels (mostly positive)
]
)
biomarker_levels = np.clip(biomarker_levels, 0, 100)
# Generate binary outcomes with logistic probability
true_probs = 1 / (1 + np.exp(-0.08 * (biomarker_levels - 50)))
y = (np.random.random(n_samples) < true_probs).astype(int)
# Fit logistic regression using gradient descent
X = (biomarker_levels - biomarker_levels.mean()) / biomarker_levels.std()
b0, b1 = 0.0, 0.0
learning_rate = 0.1
for _ in range(1000):
z = b0 + b1 * X
p = 1 / (1 + np.exp(-np.clip(z, -500, 500)))
grad_b0 = np.mean(p - y)
grad_b1 = np.mean((p - y) * X)
b0 -= learning_rate * grad_b0
b1 -= learning_rate * grad_b1
# Generate smooth curve for predictions
x_curve = np.linspace(0, 100, 100)
x_curve_norm = (x_curve - biomarker_levels.mean()) / biomarker_levels.std()
y_proba = 1 / (1 + np.exp(-np.clip(b0 + b1 * x_curve_norm, -500, 500)))
# Confidence interval (approximate using binomial SE)
se = np.sqrt(y_proba * (1 - y_proba) / n_samples) * 1.5
ci_lower = np.clip(y_proba - 1.96 * se, 0, 1)
ci_upper = np.clip(y_proba + 1.96 * se, 0, 1)
# Jitter y values for visibility
y_jittered = y + np.random.uniform(-0.025, 0.025, n_samples)
# Custom style for large canvas with theme-adaptive colors
custom_style = Style(
background=PAGE_BG,
plot_background=PAGE_BG,
foreground=INK,
foreground_strong=INK,
foreground_subtle=INK_MUTED,
colors=IMPRINT,
title_font_size=28,
label_font_size=22,
major_label_font_size=18,
legend_font_size=16,
value_font_size=14,
stroke_width=3,
)
# Create XY chart
chart = pygal.XY(
width=4800,
height=2700,
style=custom_style,
title="logistic-regression · pygal · pyplots.ai",
x_title="Biomarker Level",
y_title="Probability of Disease",
show_dots=True,
stroke=True,
show_x_guides=True,
show_y_guides=True,
dots_size=8,
stroke_style={"width": 4},
range=(0, 1.05),
xrange=(-5, 105),
explicit_size=True,
legend_at_bottom=True,
legend_box_size=28,
truncate_legend=-1,
print_values=False,
)
# Add logistic regression curve (main feature) - Okabe-Ito position 1 (green)
curve_points = [(float(x_curve[i]), float(y_proba[i])) for i in range(len(x_curve))]
chart.add("Logistic Fit", curve_points, stroke_style={"width": 5}, dots_size=0, show_dots=False, color=IMPRINT[0])
# Add confidence interval bounds - lighter/dashed variations
ci_upper_pts = [(float(x_curve[i]), float(ci_upper[i])) for i in range(0, len(x_curve), 2)]
ci_lower_pts = [(float(x_curve[i]), float(ci_lower[i])) for i in range(0, len(x_curve), 2)]
chart.add(
"95% CI Upper",
ci_upper_pts,
stroke_style={"width": 2, "dasharray": "8,4"},
dots_size=0,
show_dots=False,
color=IMPRINT[0],
)
chart.add(
"95% CI Lower",
ci_lower_pts,
stroke_style={"width": 2, "dasharray": "8,4"},
dots_size=0,
show_dots=False,
color=IMPRINT[0],
)
# Add decision threshold line (y = 0.5)
threshold_pts = [(0.0, 0.5), (100.0, 0.5)]
chart.add(
"Threshold (p=0.5)",
threshold_pts,
stroke_style={"width": 3, "dasharray": "12,6"},
dots_size=0,
show_dots=False,
color=INK_MUTED,
)
# Add data points - Negative (Class 0) - Okabe-Ito position 2 (vermillion)
negative_pts = [(float(biomarker_levels[i]), float(y_jittered[i])) for i in range(n_samples) if y[i] == 0]
chart.add("Negative (0)", negative_pts, stroke=False, dots_size=12, color=IMPRINT[1])
# Add data points - Positive (Class 1) - Okabe-Ito position 3 (blue)
positive_pts = [(float(biomarker_levels[i]), float(y_jittered[i])) for i in range(n_samples) if y[i] == 1]
chart.add("Positive (1)", positive_pts, stroke=False, dots_size=12, color=IMPRINT[2])
# Save as PNG and HTML with theme suffix
chart.render_to_png(f"plot-{THEME}.png")
with open(f"plot-{THEME}.html", "wb") as f:
f.write(chart.render())
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/logistic-regression/pygal/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "logistic-regression",
"language": "python",
"library": "pygal",
"page": "https://anyplot.ai/logistic-regression/python/pygal",
"hub": "https://anyplot.ai/logistic-regression",
"code_json": "https://api.anyplot.ai/specs/logistic-regression/pygal/code",
"spec_json": "https://api.anyplot.ai/specs/logistic-regression",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/pygal/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/pygal/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/pygal/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/pygal/plot-dark.html",
"quality_score": 86.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Logistic Regression Curve Plot on anyplot.ai.