FeatureImportance

Planned · @experimental

Planned Component

This component is part of the faststrap[plots] / faststrap[ml] optional extra and has not been implemented yet. The documentation is a preview of the planned API.

Renders a horizontal bar chart showing feature importance scores. Compatible with sklearn tree-based models, SHAP values, and custom importance arrays.

Quick Start

from faststrap.components.advance import FeatureImportance
import numpy as np

FeatureImportance(
    features=["age", "income", "score", "hours"],
    importances=[0.35, 0.28, 0.22, 0.15],
    title="Feature Importance — Random Forest",
)

Features

Horizontal bar chart sorted by importance (highest at top)
Auto-scales to percentage or raw importance values
Optional top-N feature limit
Color-coded bars (Bootstrap variant or custom)
Accepts sklearn model .feature_importances_ directly

Parameters

Parameter	Type	Default	Description
`features`	`list[str]`	required	Feature names
`importances`	`list[float]`	required	Importance scores (same order as features)
`model`	`Any \\| None`	`None`	sklearn model (extracts `.feature_importances_` and `.feature_names_in_`)
`top_n`	`int \\| None`	`None`	Limit to top N features
`normalize`	`bool`	`True`	Scale importances to 0–100%
`title`	`str \\| None`	`None`	Optional chart title
`bar_variant`	`str`	`"primary"`	Bootstrap color variant for bars
`responsive`	`bool`	`True`	Full-width responsive wrapper
`**kwargs`	`Any`		Extra wrapper attributes

Notes

Pure server-rendered — no JS required.
Marked @experimental.