uber/manifold

Health v2 · maintenance only

Adoption not part of the score

1672 stars · 116 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Manifold is a model-agnostic visual debugging tool for machine learning from Uber. It helps ML practitioners identify which subsets of data a model predicts poorly and explains potential causes by visualizing feature distribution differences between better- and worse-performing segments.

Use cases

• debug why my ML model performs poorly on certain data subsets
• visualize feature distributions across model performance segments
• compare predictions from multiple models on an evaluation dataset
• find which data slices a classifier gets wrong
• explain model errors beyond summary metrics like AUC or RMSE
• embed an ML performance visualization component in my own web app
• upload a CSV of predictions and ground truth to analyze model behavior

When to choose

• you need to visually diagnose which data subsets a model mispredicts
• you want a model-agnostic tool that works with any classifier's predictions
• you want to embed an interactive ML debugging visualization in a React web app
• you have evaluation data with features, predictions, and ground truth in CSV or JS format

When to avoid

• you need automated model training or hyperparameter tuning rather than debugging
• you need real-time monitoring of models in production
• your datasets are far larger than the recommended 10k-15k instances without sampling
• you need a non-visual, programmatic error analysis library

Facets

application · maturity maintenance

data-visualization machine-learning analytics machine-learning data-visualization data-science cross-platform visual-debugging model-agnostic ml-debugging uber react-component incubation web nodejs

Member repositories

For agents

markdown · JSON · MCP: product_card(name="uber/manifold")