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Scikit-learn VS Extism

Compare Scikit-learn VS Extism and see what are their differences

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Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Extism logo Extism

Extism is the open source, universal plug-in system. Extend all the software everywhere! Powered by WebAssembly.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Extism Landing page
    Landing page //
    2022-12-14

Scikit-learn features and specs

  • Ease of Use
    Scikit-learn provides a high-level interface for common machine learning algorithms, making it easy for beginners and professionals to implement complex models with minimal coding.
  • Extensive Documentation and Community Support
    The library has comprehensive documentation and a large, active community. This makes it easy to find tutorials, examples, and solutions to common problems.
  • Integration with Other Libraries
    Scikit-learn integrates well with other scientific computing libraries such as NumPy, SciPy, and pandas, allowing for seamless data manipulation and analysis.
  • Variety of Algorithms
    It offers a wide array of machine learning algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
  • Performance
    Designed with performance in mind, many of the algorithms are optimized and some even support multicore processing.

Possible disadvantages of Scikit-learn

  • Limited Deep Learning Support
    Scikit-learn is primarily focused on traditional machine learning algorithms and does not offer support for deep learning models, unlike libraries like TensorFlow or PyTorch.
  • Not Ideal for Large-Scale Data
    While Scikit-learn performs well for moderate-sized datasets, it may not be the best choice for extremely large datasets or big data applications.
  • Lack of Online Learning Algorithms
    The library has limited support for online learning algorithms, which are useful for scenarios where data arrives in a stream and model needs to be updated incrementally.
  • Less Flexibility in Customization
    It can be less flexible compared to lower-level libraries when highly customized or specific implementations are needed.
  • Dependency Overhead
    Scikit-learn relies on several other Python libraries like NumPy and SciPy, which might require users to manage multiple dependencies.

Extism features and specs

No features have been listed yet.

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

  • Review - Python Machine Learning Review | Learn python for machine learning. Learn Scikit-learn.

Extism videos

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Category Popularity

0-100% (relative to Scikit-learn and Extism)
Data Science And Machine Learning
Application And Data
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Software Development
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Scikit-learn and Extism

Scikit-learn Reviews

15 data science tools to consider using in 2021
Scikit-learn is an open source machine learning library for Python that's built on the SciPy and NumPy scientific computing libraries, plus Matplotlib for plotting data. It supports both supervised and unsupervised machine learning and includes numerous algorithms and models, called estimators in scikit-learn parlance. Additionally, it provides functionality for model...

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Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Extism. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Scikit-learn mentions (31)

  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 3 months ago
  • 🚀 Launching a High-Performance DistilBERT-Based Sentiment Analysis Model for Steam Reviews 🎮🤖
    Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 5 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 11 months ago
  • How to Build a Logistic Regression Model: A Spam-filter Tutorial
    Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
  • Link Prediction With node2vec in Physics Collaboration Network
    Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
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Extism mentions (19)

  • Hyperlight WASM: Fast, secure, and OS-free
    I started using WebAssembly in earnest a few months ago to make a backend auth library that works in several different languages[0]. It's built on Extism[1], which abstracts away some of the interfacing complexity. It's been an awesome experience. Frequently feels like magic. WASM is in an interesting place. The value has clearly been proved with a pretty minimal core spec. Now there's a big push to implement a... - Source: Hacker News / about 1 month ago
  • WASM Will Replace Containers
    Application plugins could also be wasm. That lets plugin authors write in any language they want and have their plugin work. That's the idea behind the Extism framework: https://extism.org/. - Source: Hacker News / 3 months ago
  • Lua Is So Underrated
    The WebAssembly component model is aimed at having composable components that can call each other. The components can be written in any language, compiled to WebAssembly, and interoperate: https://github.com/WebAssembly/component-model/ https://github.com/extism/extism A project to bring WebAssembly plugins to Godot: https://github.com/ashtonmeuser/godot-wasm Wasmer can be embedded in applications:... - Source: Hacker News / 4 months ago
  • WASM Is the New CGI
    This is exactly what we created Extism[0] and XTP[1] for! [0]: https://extism.org. - Source: Hacker News / 7 months ago
  • Running Untrusted JavaScript Code
    This is an exciting option as it provides a sandboxed environment to run code. One caveat is that you need an environment with Javascript bindings. However, an interesting project called Extism facilitates that. You might want to follow their tutorial. - Source: dev.to / 10 months ago
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What are some alternatives?

When comparing Scikit-learn and Extism, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

OpenCL - Application and Data, Languages & Frameworks, and Language Extensions

OpenCV - OpenCV is the world's biggest computer vision library

Wasmer - The Universal WebAssembly Runtime

NumPy - NumPy is the fundamental package for scientific computing with Python

Docker Compose - Define and run multi-container applications with Docker