The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, 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: matplotlib 3.10.9 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-18
"""
import os
import sys
sys.path = [p for p in sys.path if "implementations" not in p]
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
# 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 (first series is brand green)
BRAND = "#009E73"
SECONDARY = "#C475FD"
# Data - Credit risk scoring: probability of loan approval based on credit score
np.random.seed(42)
n_points = 200
# Generate credit scores (300-850 range, typical credit score range)
credit_scores = np.concatenate([np.random.normal(550, 80, n_points // 2), np.random.normal(700, 60, n_points // 2)])
credit_scores = np.clip(credit_scores, 300, 850)
# Generate binary outcomes with logistic probability
true_probs = 1 / (1 + np.exp(-0.02 * (credit_scores - 620)))
y = (np.random.random(n_points) < true_probs).astype(int)
# Fit logistic regression model
X = credit_scores.reshape(-1, 1)
model = LogisticRegression()
model.fit(X, y)
# Generate smooth curve for predictions
x_curve = np.linspace(300, 850, 300)
y_probs = model.predict_proba(x_curve.reshape(-1, 1))[:, 1]
# Calculate confidence intervals (using standard error approximation)
p = y_probs
se = np.sqrt(p * (1 - p) / n_points) * 2
ci_lower = np.clip(y_probs - 1.96 * se, 0, 1)
ci_upper = np.clip(y_probs + 1.96 * se, 0, 1)
# Calculate accuracy
y_pred = model.predict(X)
accuracy = accuracy_score(y, y_pred)
# Jitter y values for visibility
y_jittered = y + np.random.uniform(-0.03, 0.03, n_points)
# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Confidence interval band
ax.fill_between(x_curve, ci_lower, ci_upper, alpha=0.25, color=BRAND, label="95% CI")
# Logistic curve
ax.plot(x_curve, y_probs, color=BRAND, linewidth=3.5, label="Logistic Fit", zorder=3)
# Decision threshold line
ax.axhline(y=0.5, color=INK_SOFT, linestyle="--", linewidth=2, label="Decision Threshold (0.5)")
# Data points - class 0 (rejected)
mask_0 = y == 0
ax.scatter(
credit_scores[mask_0],
y_jittered[mask_0],
s=120,
alpha=0.6,
color=SECONDARY,
label="Rejected (0)",
edgecolors=PAGE_BG,
linewidth=0.5,
zorder=2,
)
# Data points - class 1 (approved)
mask_1 = y == 1
ax.scatter(
credit_scores[mask_1],
y_jittered[mask_1],
s=120,
alpha=0.6,
color=BRAND,
label="Approved (1)",
edgecolors=PAGE_BG,
linewidth=0.5,
zorder=2,
)
# Model annotation with theme-adaptive styling
coef = model.coef_[0][0]
intercept = model.intercept_[0]
annotation_text = f"Accuracy: {accuracy:.1%}\nCoef: {coef:.4f}\nIntercept: {intercept:.2f}"
ax.annotate(
annotation_text,
xy=(0.03, 0.97),
xycoords="axes fraction",
fontsize=14,
color=INK,
verticalalignment="top",
bbox={"boxstyle": "round,pad=0.5", "facecolor": ELEVATED_BG, "alpha": 0.9, "edgecolor": INK_SOFT},
)
# Labels and styling
ax.set_xlabel("Credit Score", fontsize=20, color=INK)
ax.set_ylabel("Probability of Approval", fontsize=20, color=INK)
ax.set_title("logistic-regression · python · matplotlib · anyplot.ai", fontsize=24, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax.set_xlim(300, 850)
ax.set_ylim(-0.08, 1.08)
ax.set_yticks([0, 0.25, 0.5, 0.75, 1.0])
# Spine styling
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
# Grid styling
ax.grid(True, alpha=0.15, linestyle="-", linewidth=0.8, color=INK_SOFT)
ax.set_axisbelow(True)
# Legend styling
leg = ax.legend(fontsize=16, loc="lower right")
if leg:
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
leg.get_frame().set_linewidth(0.8)
for text in leg.get_texts():
text.set_color(INK_SOFT)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/logistic-regression/matplotlib/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": "matplotlib",
"page": "https://anyplot.ai/logistic-regression/python/matplotlib",
"hub": "https://anyplot.ai/logistic-regression",
"code_json": "https://api.anyplot.ai/specs/logistic-regression/matplotlib/code",
"spec_json": "https://api.anyplot.ai/specs/logistic-regression",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/matplotlib/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/matplotlib/plot-dark.png",
"quality_score": 90.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Logistic Regression Curve Plot on anyplot.ai.