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A model-agnostic visual debugging tool for machine learning
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What is Manifold?

Understanding ML model performance and behavior is a non-trivial process, given the intrisic opacity of ML algorithms. Performance summary statistics such as AUC, RMSE, and others are not instructive enough for identifying what went wrong with a model or how to improve it. As a visual analytics tool, Manifold allows ML practitioners to look beyond overall summary metrics to detect which subset of data a model is inaccurately predicting.
Manifold is a tool in the Machine Learning Tools category of a tech stack.
Manifold is an open source tool with 910 GitHub stars and 68 GitHub forks. Here’s a link to Manifold's open source repository on GitHub

Manifold Integrations

Why developers like Manifold?

Here’s a list of reasons why companies and developers use Manifold
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Manifold's Features

  • Performance Comparison View
  • Feature Attribution View
  • Histogram / heatmap
  • Segment groups
  • Ranking
  • Geo Feature View

Manifold Alternatives & Comparisons

What are some alternatives to Manifold?
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
ML Kit
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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Manifold's Followers
1 developers follow Manifold to keep up with related blogs and decisions.
Raj Singh