Feature Importance Bar Chart — Seaborn

A horizontal bar chart displaying feature importances from machine learning models, with features sorted by importance value and bars colored by a gradient to emphasize relative contribution. This visualization is essential for model interpretability, helping data scientists and stakeholders understand which features drive model predictions. The horizontal orientation allows for readable feature names of varying lengths.

Feature Importance Bar Chart rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
bar-feature-importance: Feature Importance Bar Chart
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 85/100 | Updated: 2026-05-10
"""

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


# Data: Simulated feature importances from a Random Forest model
np.random.seed(42)

features = [
    "Annual Income",
    "Credit Score",
    "Employment Years",
    "Debt-to-Income Ratio",
    "Age",
    "Number of Accounts",
    "Loan Amount",
    "Payment History",
    "Credit Utilization",
    "Home Ownership",
    "Education Level",
    "Marital Status",
    "Monthly Expenses",
    "Savings Balance",
    "Previous Defaults",
]

# Generate realistic importance values (sum to 1.0 for interpretability)
raw_importance = np.array([0.18, 0.15, 0.12, 0.11, 0.09, 0.08, 0.07, 0.06, 0.05, 0.03, 0.02, 0.015, 0.01, 0.008, 0.007])
importance = raw_importance / raw_importance.sum()

# Standard deviation for error bars (ensemble variability)
std = np.random.uniform(0.005, 0.025, len(features))

# Create DataFrame and sort by importance
df = pd.DataFrame({"feature": features, "importance": importance, "std": std})
df = df.sort_values("importance", ascending=True).reset_index(drop=True)

# Create figure
fig, ax = plt.subplots(figsize=(16, 9))

# Create color palette based on importance values (sequential gradient)
colors = sns.color_palette("Blues", n_colors=len(df))

# Plot horizontal bars using seaborn
sns.barplot(
    data=df,
    x="importance",
    y="feature",
    hue="feature",
    palette=colors,
    legend=False,
    ax=ax,
    edgecolor="#306998",
    linewidth=1.5,
)

# Add error bars manually for ensemble variability
ax.errorbar(
    df["importance"], range(len(df)), xerr=df["std"], fmt="none", color="#306998", capsize=4, capthick=2, linewidth=2
)

# Add value annotations at the end of bars
for i, (imp, std_val) in enumerate(zip(df["importance"], df["std"], strict=True)):
    ax.text(
        imp + std_val + 0.008, i, f"{imp:.3f}", va="center", ha="left", fontsize=14, color="#306998", fontweight="bold"
    )

# Styling
ax.set_xlabel("Feature Importance", fontsize=20)
ax.set_ylabel("Feature", fontsize=20)
ax.set_title("bar-feature-importance · seaborn · pyplots.ai", fontsize=24, fontweight="bold", pad=20)
ax.tick_params(axis="both", labelsize=16)
ax.set_xlim(0, df["importance"].max() + df["std"].max() + 0.05)

# Subtle grid on x-axis only
ax.grid(True, axis="x", alpha=0.3, linestyle="--")
ax.set_axisbelow(True)

# Remove top and right spines for cleaner look
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout()
plt.savefig("plot.png", dpi=300, bbox_inches="tight")

Part of Feature Importance Bar Chart on anyplot.ai.

Feature Importance Bar Chart — Seaborn

Python source (Seaborn)

Other implementations