Model Learning Curve — Seaborn

A learning curve visualizes model performance (training and validation scores) as a function of training set size. It is essential for diagnosing bias vs variance tradeoffs, determining whether collecting more data would improve model performance, and guiding model selection decisions. The plot typically shows two lines with shaded confidence bands representing variability across cross-validation folds.

Model Learning Curve rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
learning-curve-basic: Model Learning Curve
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-10
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns


# 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 always #009E73)
TRAIN_COLOR = "#009E73"
VAL_COLOR = "#C475FD"

# Data - Simulating a learning curve with typical patterns
np.random.seed(42)

# Training set sizes
train_sizes = np.array([50, 100, 200, 400, 600, 800, 1000, 1200, 1500, 2000])
n_sizes = len(train_sizes)
n_folds = 5

# Generate realistic learning curve pattern:
# - Training score starts high and slightly decreases (model fits less perfectly with more data)
# - Validation score starts low and increases (model generalizes better with more data)
# - Gap narrows as training size increases

# Training scores - high and slightly decreasing
train_base = 0.98 - 0.03 * (train_sizes / train_sizes.max())
train_scores = np.array([train_base + np.random.normal(0, 0.01, n_sizes) for _ in range(n_folds)])
train_scores = np.clip(train_scores, 0.85, 1.0)

# Validation scores - starts lower, increases with more data
val_base = 0.65 + 0.25 * (1 - np.exp(-train_sizes / 500))
validation_scores = np.array([val_base + np.random.normal(0, 0.02, n_sizes) for _ in range(n_folds)])
validation_scores = np.clip(validation_scores, 0.55, 0.95)

# Calculate means and standard deviations
train_mean = train_scores.mean(axis=0)
train_std = train_scores.std(axis=0)
val_mean = validation_scores.mean(axis=0)
val_std = validation_scores.std(axis=0)

# Configure seaborn theme with adaptive colors
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,
    },
)
sns.set_context("talk", font_scale=1.1)

# Plot setup
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)

# Plot training curve with confidence band
ax.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, alpha=0.2, color=TRAIN_COLOR)
sns.lineplot(
    x=train_sizes,
    y=train_mean,
    ax=ax,
    color=TRAIN_COLOR,
    linewidth=3,
    marker="o",
    markersize=10,
    label="Training Score",
)

# Plot validation curve with confidence band
ax.fill_between(train_sizes, val_mean - val_std, val_mean + val_std, alpha=0.2, color=VAL_COLOR)
sns.lineplot(
    x=train_sizes, y=val_mean, ax=ax, color=VAL_COLOR, linewidth=3, marker="s", markersize=10, label="Validation Score"
)

# Labels and styling
ax.set_xlabel("Training Set Size (samples)", fontsize=20, color=INK)
ax.set_ylabel("Accuracy Score (0-1)", fontsize=20, color=INK)
ax.set_title("learning-curve-basic · seaborn · anyplot.ai", fontsize=24, color=INK, fontweight="medium")
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

# Set y-axis limits for better visualization
ax.set_ylim(0.5, 1.02)

# Configure legend
ax.legend(fontsize=16, loc="lower right", framealpha=0.95)

# Subtle grid (y-axis only)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8)

# 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)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

Part of Model Learning Curve on anyplot.ai.

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