The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, 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: plotly 6.7.0 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-18
"""
import os
import sys
# Remove current directory from path to avoid importing local plotly.py
sys.path = [p for p in sys.path if p not in ("", ".", os.path.dirname(__file__))]
import numpy as np
import plotly.graph_objects as go
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 palette
BRAND = "#009E73" # First series - bluish green
ACCENT = "#C475FD" # Second series - vermillion
# Data - medical biomarker vs disease diagnosis
np.random.seed(42)
n_samples = 150
# Generate biomarker values (e.g., cholesterol, glucose level)
biomarker = np.concatenate(
[
np.random.normal(150, 25, n_samples // 2), # Patients without disease
np.random.normal(220, 30, n_samples // 2), # Patients with disease
]
)
biomarker = np.clip(biomarker, 80, 300)
# Binary outcome (0=no disease, 1=disease present)
y = np.array([0] * (n_samples // 2) + [1] * (n_samples // 2))
# Shuffle data
shuffle_idx = np.random.permutation(len(biomarker))
biomarker = biomarker[shuffle_idx]
y = y[shuffle_idx]
# Fit logistic regression
X = biomarker.reshape(-1, 1)
model = LogisticRegression()
model.fit(X, y)
# Generate smooth curve for predictions
x_curve = np.linspace(80, 300, 200)
y_proba = model.predict_proba(x_curve.reshape(-1, 1))[:, 1]
# Calculate confidence intervals (approximate using standard error)
se = np.sqrt(y_proba * (1 - y_proba) / n_samples) * 1.96
y_upper = np.clip(y_proba + se, 0, 1)
y_lower = np.clip(y_proba - se, 0, 1)
# Jitter y values for visibility (constrained to [0, 1])
y_jittered = y + np.random.uniform(-0.02, 0.02, len(y))
y_jittered = np.clip(y_jittered, 0, 1)
# Model accuracy
accuracy = model.score(X, y)
# Create figure
fig = go.Figure()
# Confidence interval band
fig.add_trace(
go.Scatter(
x=np.concatenate([x_curve, x_curve[::-1]]),
y=np.concatenate([y_upper, y_lower[::-1]]),
fill="toself",
fillcolor="rgba(0, 158, 115, 0.2)",
line={"color": "rgba(0,0,0,0)"},
name="95% CI",
showlegend=True,
hoverinfo="skip",
)
)
# Logistic regression curve
fig.add_trace(
go.Scatter(
x=x_curve,
y=y_proba,
mode="lines",
line={"color": BRAND, "width": 4},
name="Logistic Curve",
hovertemplate="<b>Biomarker:</b> %{x:.1f}<br><b>Probability:</b> %{y:.3f}<extra></extra>",
)
)
# Decision threshold line at 0.5
fig.add_trace(
go.Scatter(
x=[80, 300],
y=[0.5, 0.5],
mode="lines",
line={"color": INK_SOFT, "width": 2, "dash": "dash"},
name="Decision Threshold (0.5)",
hoverinfo="skip",
)
)
# Data points - Class 0 (No Disease)
mask_0 = y == 0
fig.add_trace(
go.Scatter(
x=biomarker[mask_0],
y=y_jittered[mask_0],
mode="markers",
marker={"size": 12, "color": BRAND, "opacity": 0.6, "line": {"width": 1, "color": PAGE_BG}},
name="No Disease (0)",
hovertemplate="<b>Biomarker:</b> %{x:.1f}<br><b>Outcome:</b> No Disease<extra></extra>",
)
)
# Data points - Class 1 (Disease Present)
mask_1 = y == 1
fig.add_trace(
go.Scatter(
x=biomarker[mask_1],
y=y_jittered[mask_1],
mode="markers",
marker={"size": 12, "color": ACCENT, "opacity": 0.6, "line": {"width": 1, "color": PAGE_BG}},
name="Disease Present (1)",
hovertemplate="<b>Biomarker:</b> %{x:.1f}<br><b>Outcome:</b> Disease Present<extra></extra>",
)
)
# Layout
fig.update_layout(
title={
"text": "logistic-regression · python · plotly · anyplot.ai",
"font": {"size": 28, "color": INK},
"x": 0.5,
"xanchor": "center",
},
xaxis={
"title": {"text": "Biomarker Level (mg/dL)", "font": {"size": 22, "color": INK}},
"tickfont": {"size": 18, "color": INK_SOFT},
"range": [75, 305],
"gridcolor": "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)",
"gridwidth": 1,
"linecolor": INK_SOFT,
"zerolinecolor": INK_SOFT,
},
yaxis={
"title": {"text": "Disease Probability", "font": {"size": 22, "color": INK}},
"tickfont": {"size": 18, "color": INK_SOFT},
"range": [-0.05, 1.05],
"gridcolor": "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)",
"gridwidth": 1,
"linecolor": INK_SOFT,
"zerolinecolor": INK_SOFT,
},
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
font={"color": INK},
legend={
"font": {"size": 16, "color": INK_SOFT},
"x": 0.02,
"y": 0.98,
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 1,
},
annotations=[
{
"x": 265,
"y": 0.15,
"text": f"Accuracy: {accuracy:.1%}",
"showarrow": False,
"font": {"size": 18, "color": INK},
"bgcolor": ELEVATED_BG,
"bordercolor": INK_SOFT,
"borderwidth": 1,
"borderpad": 6,
}
],
margin={"l": 80, "r": 60, "t": 100, "b": 80},
)
# Save as PNG (4800x2700)
fig.write_image(f"plot-{THEME}.png", width=1600, height=900, scale=3)
# Save interactive HTML
fig.write_html(f"plot-{THEME}.html", include_plotlyjs="cdn")
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/logistic-regression/plotly/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
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"language": "python",
"library": "plotly",
"page": "https://anyplot.ai/logistic-regression/python/plotly",
"hub": "https://anyplot.ai/logistic-regression",
"code_json": "https://api.anyplot.ai/specs/logistic-regression/plotly/code",
"spec_json": "https://api.anyplot.ai/specs/logistic-regression",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/plotly/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/plotly/plot-dark.png",
"interactive_light_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/plotly/plot-light.html",
"interactive_dark_html": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/plotly/plot-dark.html",
"quality_score": 93.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Logistic Regression Curve Plot on anyplot.ai.