The same plot in 14 other libraries — Python: Altair, Bokeh, Matplotlib, Plotly, plotnine, Pygal, 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: letsplot 4.9.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-18
"""
import os
import shutil
import numpy as np
import pandas as pd
from lets_plot import *
from lets_plot.export import ggsave
from sklearn.linear_model import LogisticRegression
LetsPlot.setup_html()
# Theme tokens (see prompts/default-style-guide.md "Background" + "Theme-adaptive Chrome")
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Okabe-Ito palette (first two colors for binary classification)
IMPRINT = ["#009E73", "#C475FD"]
# Data - Generate binary classification data with clear sigmoidal relationship
np.random.seed(42)
n_samples = 200
# Feature: Study hours (0 to 10 hours)
x = np.random.uniform(0, 10, n_samples)
# True probability follows a logistic function
true_prob = 1 / (1 + np.exp(-1.5 * (x - 5)))
# Binary outcome (pass/fail exam based on study hours)
y = (np.random.random(n_samples) < true_prob).astype(int)
# Fit logistic regression model using sklearn
X_reshaped = x.reshape(-1, 1)
model = LogisticRegression()
model.fit(X_reshaped, y)
# Get model parameters for annotation
coef = model.coef_[0][0]
intercept = model.intercept_[0]
accuracy = model.score(X_reshaped, y)
# Generate smooth curve for prediction
x_line = np.linspace(0, 10, 200)
X_line = x_line.reshape(-1, 1)
y_prob = model.predict_proba(X_line)[:, 1]
# Calculate confidence intervals using approximate standard error
se = np.sqrt(y_prob * (1 - y_prob) / n_samples) * 2
ci_lower = np.clip(y_prob - 1.96 * se, 0, 1)
ci_upper = np.clip(y_prob + 1.96 * se, 0, 1)
# Add jitter to y values for visibility
y_jittered = y + np.random.normal(0, 0.03, n_samples)
y_jittered = np.clip(y_jittered, -0.1, 1.1)
# Create DataFrames
df_points = pd.DataFrame(
{"Study Hours": x, "Probability": y_jittered, "Class": ["Pass" if yi == 1 else "Fail" for yi in y]}
)
df_curve = pd.DataFrame({"Study Hours": x_line, "Probability": y_prob})
df_ci = pd.DataFrame({"Study Hours": x_line, "ci_lower": ci_lower, "ci_upper": ci_upper})
# Create plot with theme-adaptive styling
anyplot_theme = theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major=element_line(color=INK, size=0.3),
panel_grid_minor=element_blank(),
axis_title=element_text(color=INK, size=20),
axis_text=element_text(color=INK_SOFT, size=16),
axis_line=element_line(color=INK_SOFT, size=0.5),
plot_title=element_text(color=INK, size=24),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT, size=16),
legend_title=element_text(color=INK, size=18),
)
plot = (
ggplot()
# Confidence interval ribbon
+ geom_ribbon(aes(x="Study Hours", ymin="ci_lower", ymax="ci_upper"), data=df_ci, fill=IMPRINT[0], alpha=0.15)
# Logistic curve
+ geom_line(aes(x="Study Hours", y="Probability"), data=df_curve, color=IMPRINT[0], size=2.5)
# Decision threshold line
+ geom_hline(yintercept=0.5, linetype="dashed", color=INK_SOFT, size=1, alpha=0.6)
# Data points with jitter
+ geom_point(aes(x="Study Hours", y="Probability", color="Class"), data=df_points, size=5, alpha=0.65)
# Colors for classes using Okabe-Ito
+ scale_color_manual(values=IMPRINT)
# Labels with model annotation
+ labs(
x="Study Hours",
y="Probability",
title="logistic-regression · python · letsplot · anyplot.ai",
color="Class",
subtitle=f"Coefficient: {coef:.2f} | Accuracy: {accuracy:.1%}",
)
# Y-axis from 0 to 1
+ scale_y_continuous(limits=[-0.1, 1.1])
# Theme and size
+ anyplot_theme
+ ggsize(1600, 900)
)
# Save as PNG and HTML with theme suffix
ggsave(plot, f"plot-{THEME}.png", scale=3)
ggsave(plot, f"plot-{THEME}.html")
# Move files from lets-plot-images subfolder to current directory
if os.path.exists("lets-plot-images"):
for filename in [f"plot-{THEME}.png", f"plot-{THEME}.html"]:
src = os.path.join("lets-plot-images", filename)
if os.path.exists(src):
shutil.move(src, filename)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/logistic-regression/letsplot/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": "letsplot",
"page": "https://anyplot.ai/logistic-regression/python/letsplot",
"hub": "https://anyplot.ai/logistic-regression",
"code_json": "https://api.anyplot.ai/specs/logistic-regression/letsplot/code",
"spec_json": "https://api.anyplot.ai/specs/logistic-regression",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/letsplot/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/letsplot/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/letsplot/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/letsplot/plot-dark.html",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Logistic Regression Curve Plot on anyplot.ai.