InterpretML fits interpretable models and explains results from black-box machine learning systems.
Open source
24 alternativesManifold
Manifold provides visual debugging for machine learning models without depending on a specific model type.
About Manifold
Manifold is a model-agnostic visual debugging tool for machine learning. It helps practitioners move beyond summary metrics such as AUC and RMSE to find data subsets where predictions are inaccurate and to surface feature distribution differences between better and worse-performing subsets.
It provides a Performance Comparison View for comparing models across automatically created segments and a Feature Attribution View for feature distributions by performance level. Data can be added by CSV upload in the demo app or programmatically as a component, using matching x, yPred, and yTrue arrays; the recommended size is 10,000–15,000 instances, with random subsampling for larger datasets.
Key features
- Compare prediction performance across models and data subsets
- Identify under-performing data subsets for further inspection
- Visualize feature distributions across performance levels
- Segment data automatically with k-Means performance clustering
- Import data by CSV upload in demo app
- Input feature, prediction, and ground-truth arrays programmatically
Best for: ML practitioners who need to locate and explain under-performing data subsets beyond summary metrics.
Good for
- Works across model types without model-specific setup
- Compares multiple models across automatically created performance segments
- Surfaces feature distribution differences between better and worse subsets
Watch out for
- Requires data in specific x, yPred, yTrue format with matching order
- Recommended for 10,000-15,000 instances; larger datasets need subsampling
Manifold at a glance
- Website
- github.com/uber/manifold
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Manifold alternatives
Tools and sites you could use instead, ranked by how closely they match.
A Python package to assess and improve fairness of machine learning models.
Open source
24 alternatives
DVCLive logs and tracks machine learning metrics, parameters, and models with Git or DVC.
Open source
24 alternatives
19 Questions assigns attributes to objects using machine learning and Bayesian inference, according to its project description.
Open source
24 alternatives
oneDAL is a C++ and DPC++ library for accelerated machine learning routines on tabular data, governed by the UXL Foundation.
Open source
24 alternatives
The LightGBM framework uses gradient boosting for ranking, classification, and other machine learning tasks.
Open source
24 alternativesPopular in AI Data & Analytics
Well-known listings from the same topic.
Dotgetdot.aiForvioforvio.comFluidAI Medicalfluidai.mdApache Zeppelinzeppelin.apache.orgSola Securitysola.securityQuadraticquadratichq.com
Manifold FAQ
How can I add data to Manifold?
Use CSV upload in the demo app, or convert data programmatically using x, yPred, and yTrue when using the component in your own app.
What format must input data follow?
x holds feature objects, yPred holds prediction lists per model, and yTrue holds ground truth; the order of instances must match across all three.
How does the Performance Comparison View group data?
It uses k-Means clustering on per-instance scores — log-loss for classification and squared-error for regression by default — to create segments based on performance similarity.
Is something wrong or out of date?
Tell us and an editor will check it.
Own this website?
Claim this listing
Claim the listing and suggest updates yourself. An editor checks every claim first.