The same plot in 14 other libraries — Python: Altair, lets-plot, 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: bokeh 3.9.0 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-18
"""
import os
# Remove the current directory from sys.path to avoid circular imports with bokeh.py
import sys
import time
from pathlib import Path
sys.path = [p for p in sys.path if p not in ("", ".", os.getcwd(), os.path.dirname(__file__))]
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, Label, Span
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
# Theme tokens
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 colors
COLOR_0 = "#009E73" # Class 0 - bluish green (brand color)
COLOR_1 = "#C475FD" # Class 1 - vermillion
# Data - Generate binary classification data
np.random.seed(42)
X, y = make_classification(
n_samples=200, n_features=1, n_informative=1, n_redundant=0, n_clusters_per_class=1, flip_y=0.1, random_state=42
)
x = X.flatten()
# Scale x to meaningful range (e.g., test score 20-80)
x = (x - x.min()) / (x.max() - x.min()) * 60 + 20
# Fit logistic regression
model = LogisticRegression()
model.fit(x.reshape(-1, 1), y)
# Generate smooth curve for logistic regression
x_curve = np.linspace(x.min() - 2, x.max() + 2, 300)
prob_curve = model.predict_proba(x_curve.reshape(-1, 1))[:, 1]
# Compute confidence intervals (using asymptotic approximation)
p_val = prob_curve
se = np.sqrt(p_val * (1 - p_val) / len(x))
z = 1.96
ci_lower = np.clip(prob_curve - z * se * 3, 0, 1)
ci_upper = np.clip(prob_curve + z * se * 3, 0, 1)
# Jitter y values for visibility
jitter = np.random.uniform(-0.03, 0.03, len(y))
y_jittered = y + jitter
# Separate data by class
class_0_mask = y == 0
class_1_mask = y == 1
# Create data sources
source_class0 = ColumnDataSource(data={"x": x[class_0_mask], "y": y_jittered[class_0_mask]})
source_class1 = ColumnDataSource(data={"x": x[class_1_mask], "y": y_jittered[class_1_mask]})
source_curve = ColumnDataSource(data={"x": x_curve, "prob": prob_curve, "ci_lower": ci_lower, "ci_upper": ci_upper})
# Create figure
p = figure(
width=4800,
height=2700,
title="logistic-regression · python · bokeh · anyplot.ai",
x_axis_label="Test Score",
y_axis_label="Probability of Passing",
x_range=(x.min() - 5, x.max() + 5),
y_range=(-0.08, 1.08),
)
# Confidence interval band
p.varea(
x="x", y1="ci_lower", y2="ci_upper", source=source_curve, fill_color=COLOR_0, fill_alpha=0.15, legend_label="95% CI"
)
# Logistic curve
p.line(x="x", y="prob", source=source_curve, line_color=COLOR_0, line_width=5, legend_label="Logistic Curve")
# Data points - Class 0
p.scatter(
x="x",
y="y",
source=source_class0,
size=20,
fill_color=COLOR_0,
fill_alpha=0.6,
line_color=PAGE_BG,
line_width=2,
legend_label="Class 0 (Failed)",
)
# Data points - Class 1
p.scatter(
x="x",
y="y",
source=source_class1,
size=20,
fill_color=COLOR_1,
fill_alpha=0.6,
line_color=PAGE_BG,
line_width=2,
legend_label="Class 1 (Passed)",
)
# Decision threshold line at p=0.5
threshold = Span(location=0.5, dimension="width", line_color=INK_SOFT, line_width=4, line_dash="dashed")
p.add_layout(threshold)
# Add threshold label
threshold_label = Label(
x=x.max() - 5,
y=0.53,
text="Decision Threshold (p=0.5)",
text_font_size="20pt",
text_color=INK_SOFT,
text_align="right",
)
p.add_layout(threshold_label)
# Model accuracy annotation
accuracy = model.score(x.reshape(-1, 1), y)
accuracy_label = Label(
x=x.min() + 2, y=0.92, text=f"Model Accuracy: {accuracy:.1%}", text_font_size="22pt", text_color=INK
)
p.add_layout(accuracy_label)
# Styling
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
# Axis styling
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_color = INK
p.xgrid.grid_line_alpha = 0.10
p.ygrid.grid_line_alpha = 0.10
# Legend styling - repositioned to top-left to avoid data overlap
p.legend.location = "top_left"
p.legend.label_text_font_size = "18pt"
p.legend.label_text_color = INK_SOFT
p.legend.glyph_height = 35
p.legend.glyph_width = 35
p.legend.spacing = 12
p.legend.background_fill_color = ELEVATED_BG
p.legend.background_fill_alpha = 0.9
p.legend.border_line_color = INK_SOFT
p.legend.padding = 15
# Background
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
# Save HTML
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with Selenium
W, H = 4800, 2700
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/logistic-regression/bokeh/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": "bokeh",
"page": "https://anyplot.ai/logistic-regression/python/bokeh",
"hub": "https://anyplot.ai/logistic-regression",
"code_json": "https://api.anyplot.ai/specs/logistic-regression/bokeh/code",
"spec_json": "https://api.anyplot.ai/specs/logistic-regression",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/bokeh/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/bokeh/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/bokeh/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/bokeh/plot-dark.html",
"quality_score": 89.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Logistic Regression Curve Plot on anyplot.ai.