feat(seaborn): implement bar-feature-importance (#2292)

## Implementation: `bar-feature-importance` - seaborn

Implements the **seaborn** version of `bar-feature-importance`.

**File:** `plots/bar-feature-importance/implementations/seaborn.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526602278)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/bar-feature-importance/implementations/seaborn.py

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+""" pyplots.ai
+bar-feature-importance: Feature Importance Bar Chart
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 93/100 | Created: 2025-12-26
+"""
+
+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")

plots/bar-feature-importance/metadata/seaborn.yaml

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+library: seaborn
+specification_id: bar-feature-importance
+created: '2025-12-26T17:37:27Z'
+updated: '2025-12-26T17:45:31Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526602278
+issue: 0
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/bar-feature-importance/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 93
+review:
+  strengths:
+  - Excellent visual clarity with proper font sizing and no text overlap
+  - Color gradient effectively communicates relative importance
+  - Error bars with caps clearly show ensemble variability
+  - Realistic credit scoring context with appropriate feature names
+  - Clean code following KISS principles with proper seaborn 0.14+ API usage
+  - Value annotations positioned well and aid interpretation
+  weaknesses:
+  - Error bars are added manually via matplotlib instead of leveraging seaborn built-in
+    errorbar capabilities