Yellowbrick vs scikit-learn

Overview

scikit-learn

Stacks1.3K

Followers1.1K

Votes45

GitHub Stars63.9K

Forks26.4K

Yellowbrick

Stacks6

Followers12

Votes0

GitHub Stars4.4K

Forks566

Yellowbrick vs scikit-learn: What are the differences?

Visualization Capabilities: Yellowbrick provides a variety of visualization tools specifically designed for assessing and analyzing machine learning models, such as visualizing feature importances, model selection, and hyperparameter tuning, which are not readily available in scikit-learn.
Interactive Plots: Yellowbrick offers interactive visualizations that allow users to explore and interact with the results dynamically, providing a more engaging and intuitive experience compared to the static visualizations in scikit-learn.
Model Evaluation: While scikit-learn provides a range of metrics and tools for evaluating models, Yellowbrick focuses on providing visual diagnostic tools for model evaluation, making it easier to identify potential issues and areas for improvement.
Ease of Use: Scikit-learn is more suitable for building and training machine learning models, while Yellowbrick is geared towards enhancing the interpretability and explainability of these models through visualizations, making it more user-friendly in terms of model evaluation and analysis.
Integration: Scikit-learn is widely used as a core library for machine learning tasks, while Yellowbrick is designed to complement scikit-learn by providing additional visual analysis capabilities, making it easy to integrate into existing machine learning workflows.
Community Support: Scikit-learn has a larger and more established community with extensive documentation and resources, while Yellowbrick, being a newer library, is still growing its community but offers unique visual analysis tools that can add value to machine learning projects.

In Summary, Yellowbrick focuses on visualizing machine learning models for enhanced interpretability, while scikit-learn is a comprehensive library for building and training machine learning models.

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Manual

Detailed Comparison

scikit-learn	Yellowbrick
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.	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.
-	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.
Statistics
GitHub Stars 63.9K	GitHub Stars 4.4K
GitHub Forks 26.4K	GitHub Forks 566
Stacks 1.3K	Stacks 6
Followers 1.1K	Followers 12
Votes 45	Votes 0
Pros & Cons
Pros 26 Scientific computing 19 Easy Cons 2 Limited	No community feedback yet
Integrations
No integrations available	Matplotlib

What are some alternatives to scikit-learn, Yellowbrick?

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.

PyTorch

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.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

Related Comparisons

Yellowbrick vs scikit-learn: What are the differences?

Visualization Capabilities: Yellowbrick provides a variety of visualization tools specifically designed for assessing and analyzing machine learning models, such as visualizing feature importances, model selection, and hyperparameter tuning, which are not readily available in scikit-learn.
Interactive Plots: Yellowbrick offers interactive visualizations that allow users to explore and interact with the results dynamically, providing a more engaging and intuitive experience compared to the static visualizations in scikit-learn.
Model Evaluation: While scikit-learn provides a range of metrics and tools for evaluating models, Yellowbrick focuses on providing visual diagnostic tools for model evaluation, making it easier to identify potential issues and areas for improvement.
Ease of Use: Scikit-learn is more suitable for building and training machine learning models, while Yellowbrick is geared towards enhancing the interpretability and explainability of these models through visualizations, making it more user-friendly in terms of model evaluation and analysis.
Integration: Scikit-learn is widely used as a core library for machine learning tasks, while Yellowbrick is designed to complement scikit-learn by providing additional visual analysis capabilities, making it easy to integrate into existing machine learning workflows.
Community Support: Scikit-learn has a larger and more established community with extensive documentation and resources, while Yellowbrick, being a newer library, is still growing its community but offers unique visual analysis tools that can add value to machine learning projects.

In Summary, Yellowbrick focuses on visualizing machine learning models for enhanced interpretability, while scikit-learn is a comprehensive library for building and training machine learning models.

Yellowbrick vs scikit-learn

Overview