Streamlit vs Yellowbrick

Overview

Streamlit

Stacks404

Followers407

Votes12

GitHub Stars42.1K

Forks3.9K

Yellowbrick

Stacks6

Followers12

Votes0

GitHub Stars4.4K

Forks566

Streamlit vs Yellowbrick: What are the differences?

Introduction

Streamlit and Yellowbrick are both tools used in the field of data visualization and machine learning, but they serve different purposes and have unique features that set them apart.

Purpose: Streamlit is primarily a tool used for building interactive web applications for machine learning and data science projects. It allows users to easily create and share interactive data visualizations and applications using simple Python scripts. On the other hand, Yellowbrick is a visualization tool specifically designed to enhance the machine learning workflow by providing a set of visualizers for model evaluation, feature analysis, hyperparameter tuning, etc.
Ease of Use: Streamlit excels in its user-friendly interface and simplicity for creating interactive visualizations and web applications without requiring extensive knowledge of web development. In contrast, Yellowbrick provides specialized visualizers that integrate seamlessly with popular machine learning libraries like scikit-learn, enabling users to generate advanced visualizations with minimal effort.
Customization: Streamlit offers a high level of customization for designing the layout, style, and functionality of web applications, allowing users to create tailored and visually appealing interfaces. Yellowbrick, on the other hand, focuses on providing a specific set of visualization tools targeted at machine learning tasks, which limits the level of customization available for users.
Integration: Streamlit is designed to work well with various data science and machine learning libraries, making it easy to integrate with existing workflows and models. Yellowbrick integrates seamlessly with scikit-learn and other machine learning libraries, providing specific visualizers that enhance the model evaluation and selection process.
Community and Support: Streamlit has a large and active community of users and contributors, providing extensive documentation, tutorials, and support forums for users to collaborate and troubleshoot issues. Yellowbrick also has a supportive community, albeit smaller compared to Streamlit, with a focus on enhancing the machine learning visualization experience.
Performance: Streamlit is optimized for building fast and responsive web applications, ensuring smooth user interaction and real-time updates. Yellowbrick focuses on providing high-quality visualizations for analyzing machine learning models, which may not prioritize real-time performance in the same way as Streamlit.

In Summary, Streamlit excels in creating interactive web applications, while Yellowbrick specializes in providing visualizers for enhancing the machine learning workflow.

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CLI (Node.js)

Manual

Detailed Comparison

Streamlit	Yellowbrick
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.	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.
Free and open source; Build apps in a dozen lines of Python with a simple API; No callbacks; No hidden state; Works with TensorFlow, Keras, PyTorch, Pandas, Numpy, Matplotlib, Seaborn, Altair, Plotly, Bokeh, Vega-Lite, and more	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 42.1K	GitHub Stars 4.4K
GitHub Forks 3.9K	GitHub Forks 566
Stacks 404	Stacks 6
Followers 407	Followers 12
Votes 12	Votes 0
Pros & Cons
Pros 11 Fast development 1 Fast development and apprenticeship	No community feedback yet
Integrations
Python Plotly.js PyTorch Pandas Bokeh Keras NumPy Matplotlib TensorFlow Altair GraphQL	Matplotlib scikit-learn

What are some alternatives to Streamlit, 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.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

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.

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.

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Streamlit vs Yellowbrick: What are the differences?

Introduction

Streamlit and Yellowbrick are both tools used in the field of data visualization and machine learning, but they serve different purposes and have unique features that set them apart.

Purpose: Streamlit is primarily a tool used for building interactive web applications for machine learning and data science projects. It allows users to easily create and share interactive data visualizations and applications using simple Python scripts. On the other hand, Yellowbrick is a visualization tool specifically designed to enhance the machine learning workflow by providing a set of visualizers for model evaluation, feature analysis, hyperparameter tuning, etc.
Ease of Use: Streamlit excels in its user-friendly interface and simplicity for creating interactive visualizations and web applications without requiring extensive knowledge of web development. In contrast, Yellowbrick provides specialized visualizers that integrate seamlessly with popular machine learning libraries like scikit-learn, enabling users to generate advanced visualizations with minimal effort.
Customization: Streamlit offers a high level of customization for designing the layout, style, and functionality of web applications, allowing users to create tailored and visually appealing interfaces. Yellowbrick, on the other hand, focuses on providing a specific set of visualization tools targeted at machine learning tasks, which limits the level of customization available for users.
Integration: Streamlit is designed to work well with various data science and machine learning libraries, making it easy to integrate with existing workflows and models. Yellowbrick integrates seamlessly with scikit-learn and other machine learning libraries, providing specific visualizers that enhance the model evaluation and selection process.
Community and Support: Streamlit has a large and active community of users and contributors, providing extensive documentation, tutorials, and support forums for users to collaborate and troubleshoot issues. Yellowbrick also has a supportive community, albeit smaller compared to Streamlit, with a focus on enhancing the machine learning visualization experience.
Performance: Streamlit is optimized for building fast and responsive web applications, ensuring smooth user interaction and real-time updates. Yellowbrick focuses on providing high-quality visualizations for analyzing machine learning models, which may not prioritize real-time performance in the same way as Streamlit.

In Summary, Streamlit excels in creating interactive web applications, while Yellowbrick specializes in providing visualizers for enhancing the machine learning workflow.