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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: seaborn 0.13.2 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-18
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from scipy.special import expit
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
COLOR_CLASS_0 = "#009E73"
COLOR_CLASS_1 = "#C475FD"
# Data
np.random.seed(42)
n_samples = 200
x = np.random.uniform(-3, 3, n_samples)
true_prob = expit(1.5 * x + 0.5)
y = (np.random.random(n_samples) < true_prob).astype(int)
X_train = x.reshape(-1, 1)
model = LogisticRegression()
model.fit(X_train, y)
x_curve = np.linspace(-3.5, 3.5, 300)
X_curve = x_curve.reshape(-1, 1)
prob_curve = model.predict_proba(X_curve)[:, 1]
n_bootstrap = 100
bootstrap_probs = np.zeros((n_bootstrap, len(x_curve)))
for i in range(n_bootstrap):
idx = np.random.choice(n_samples, n_samples, replace=True)
X_boot = x[idx].reshape(-1, 1)
y_boot = y[idx]
model_boot = LogisticRegression()
model_boot.fit(X_boot, y_boot)
bootstrap_probs[i] = model_boot.predict_proba(X_curve)[:, 1]
ci_lower = np.percentile(bootstrap_probs, 2.5, axis=0)
ci_upper = np.percentile(bootstrap_probs, 97.5, axis=0)
y_jittered = y + np.random.uniform(-0.05, 0.05, n_samples)
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.10,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
# Create plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Confidence interval
ax.fill_between(x_curve, ci_lower, ci_upper, alpha=0.25, color=COLOR_CLASS_0)
# Logistic curve
ax.plot(x_curve, prob_curve, color=COLOR_CLASS_0, linewidth=3, zorder=5)
# Data points
class_0_mask = y == 0
class_1_mask = y == 1
ax.scatter(
x[class_0_mask],
y_jittered[class_0_mask],
s=150,
alpha=0.6,
color=COLOR_CLASS_0,
edgecolors=PAGE_BG,
linewidth=0.5,
zorder=4,
)
ax.scatter(
x[class_1_mask],
y_jittered[class_1_mask],
s=150,
alpha=0.6,
color=COLOR_CLASS_1,
edgecolors=PAGE_BG,
linewidth=0.5,
zorder=4,
)
# Decision threshold line
ax.axhline(y=0.5, color=INK_SOFT, linestyle="--", linewidth=2, zorder=3)
# Legend
legend_elements = [
Line2D([0], [0], color=COLOR_CLASS_0, linewidth=3, label="Logistic Curve"),
Patch(facecolor=COLOR_CLASS_0, alpha=0.25, label="95% CI"),
Line2D([0], [0], color=INK_SOFT, linestyle="--", linewidth=2, label="Decision Threshold (p=0.5)"),
Line2D(
[0],
[0],
marker="o",
color="w",
markerfacecolor=COLOR_CLASS_0,
markersize=12,
label="Class 0",
markeredgecolor=PAGE_BG,
markeredgewidth=0.5,
),
Line2D(
[0],
[0],
marker="o",
color="w",
markerfacecolor=COLOR_CLASS_1,
markersize=12,
label="Class 1",
markeredgecolor=PAGE_BG,
markeredgewidth=0.5,
),
]
ax.legend(handles=legend_elements, fontsize=16, loc="upper left")
# Model info annotation
accuracy = model.score(X_train, y)
coef = model.coef_[0][0]
intercept = model.intercept_[0]
ax.annotate(
f"Accuracy: {accuracy:.1%}\nCoef: {coef:.2f}, Intercept: {intercept:.2f}",
xy=(0.98, 0.02),
xycoords="axes fraction",
fontsize=14,
ha="right",
va="bottom",
bbox={"boxstyle": "round", "facecolor": ELEVATED_BG, "edgecolor": INK_SOFT, "alpha": 0.9},
color=INK,
)
# Styling
ax.set_xlabel("Predictor Variable (X)", fontsize=20, color=INK)
ax.set_ylabel("Probability", fontsize=20, color=INK)
ax.set_title("logistic-regression · python · seaborn · anyplot.ai", fontsize=24, color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)
ax.set_ylim(-0.1, 1.1)
ax.set_xlim(-3.5, 3.5)
# Remove top and right spines
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)
# Subtle grid
ax.grid(True, alpha=0.10, axis="y", linewidth=0.8)
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/seaborn/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": "seaborn",
"page": "https://anyplot.ai/logistic-regression/python/seaborn",
"hub": "https://anyplot.ai/logistic-regression",
"code_json": "https://api.anyplot.ai/specs/logistic-regression/seaborn/code",
"spec_json": "https://api.anyplot.ai/specs/logistic-regression",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/seaborn/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/logistic-regression/python/seaborn/plot-dark.png",
"quality_score": 94.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Logistic Regression Curve Plot on anyplot.ai.