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Yellowbrick

Visual analysis and diagnostic tools to facilitate machine learning model selection.
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What is Yellowbrick?

It is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, it combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow.
Yellowbrick is a tool in the Machine Learning Tools category of a tech stack.
Yellowbrick is an open source tool with 3.3K GitHub stars and 492 GitHub forks. Here’s a link to Yellowbrick's open source repository on GitHub

Who uses Yellowbrick?

Yellowbrick Integrations

Yellowbrick's Features

  • Evaluate the stability and predictive value of machine learning models and improve the speed of the experimental workflow
  • Provide visual tools for monitoring model performance in real-world applications
  • Provide visual interpretation of the behavior of the model in high dimensional feature space.

Yellowbrick Alternatives & Comparisons

What are some alternatives to Yellowbrick?
Snowflake
Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
MemSQL
MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
TensorFlow
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.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
See all alternatives

Yellowbrick's Followers
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