ModelMetrics

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.

Composed dashboard card showing comprehensive ML model evaluation metrics: accuracy, precision, recall, F1-score, and optional confusion matrix preview.

Quick Start

from faststrap.components.advance import ModelMetrics

ModelMetrics(
    title="Random Forest Evaluation",
    metrics={
        "Accuracy": "94.2%",
        "Precision": "91.8%",
        "Recall": "89.5%",
        "F1 Score": "90.6%",
    },
    confusion_matrix=[[50, 3], [4, 43]],
    confusion_matrix_labels=["Negative", "Positive"],
)

Features

Grid of named metric values with optional trend indicators
Compact confusion matrix preview below the metric grid
Bootstrap card layout with variant support
Works with sklearn classification_report output
Composable: use standalone or embed in DashboardGrid

Parameters

Parameter	Type	Default	Description
`title`	`str`	required	Card title
`metrics`	`dict[str, str \\| tuple[str, str]]`	required	Metric name → value (or value + trend tuple)
`confusion_matrix`	`list[list[int]] \\| None`	`None`	2D confusion matrix for inline preview
`confusion_matrix_labels`	`list[str] \\| None`	`None`	Class labels for the matrix
`variant`	`str \\| None`	`None`	Bootstrap card variant
`columns`	`int`	`2`	Number of metric columns per row
`responsive`	`bool`	`True`	Full-width responsive wrapper
`**kwargs`	`Any`		Extra wrapper attributes

Notes

Pure server-rendered — no JS required for the base view.
For a full confusion matrix, use the dedicated ConfusionMatrix component.
Marked @experimental.