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Scikit-learn VS PyInstaller

Compare Scikit-learn VS PyInstaller 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.

PyInstaller logo PyInstaller

PyInstaller is a program that freezes (packages) Python programs into stand-alone executables...
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • PyInstaller Landing page
    Landing page //
    2021-10-20

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.

PyInstaller features and specs

  • Cross-Platform Support
    PyInstaller supports Windows, macOS, and Linux, allowing developers to create executables for multiple platforms from a single codebase.
  • Single Executable
    PyInstaller can bundle a Python application and all its dependencies into a single executable, simplifying distribution as users do not need to install Python separately.
  • Easy to Use
    PyInstaller has straightforward commands and a simple configuration process, making it accessible even for those with limited experience in creating executables.
  • Customizable
    PyInstaller provides various options for customization, allowing developers to specify which files to include or exclude, add data files, and more.
  • Active Community
    PyInstaller benefits from an active community that contributes to its development and provides support through forums and other platforms.

Possible disadvantages of PyInstaller

  • Executable Size
    The executable files generated by PyInstaller can be large since they include the Python interpreter and all dependencies, which may not be ideal for applications with size constraints.
  • Compatibility Issues
    While PyInstaller supports many third-party Python packages, some packages may not work out of the box, requiring additional configuration or adjustments.
  • Occasional Bugs
    Like any software tool, PyInstaller can have bugs, especially with new or less common Python features, which may require troubleshooting or code workarounds.
  • Limited Optimization
    The executables produced by PyInstaller may not be as optimized in terms of performance as those created by more complex methods or tools specifically designed for performance enhancements.
  • Dynamic Module Loading
    Handling dynamic imports can be challenging with PyInstaller, requiring developers to manually specify hidden imports to ensure all dependencies are included.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

PyInstaller videos

+ Add

Archivo ejecutable en Python | Windows| PyInstaller |PyQT5| Python | ¡Muy fácil!

More videos:

  • Review - python hack #8 reverse shell espionage cmd fichier py en exe pyinstaller part2
  • Review - python hack #8 reverse shell espionage cmd fichier py en exe pyinstaller part1

Category Popularity

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Scikit-learn

PyInstaller

Data Science And Machine Learning
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Data Science And Machine Learning
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Website Builder
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Website Builder
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Data Science Tools
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Data Science Tools
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Website Design
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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 PyInstaller

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...
Source: searchbusinessanalytics.techtarget.com

PyInstaller Reviews

We have no reviews of PyInstaller yet.
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Social recommendations and mentions

PyInstaller might be a bit more popular than Scikit-learn. We know about 31 links to it since March 2021 and only 31 links to Scikit-learn. 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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PyInstaller mentions (31)

  • Cosmopolitan v3.5.0
    Looking forward toward somebody hooking together Python in APE [0], something like pex [1]/shiv[2]/pyinstaller[3], and the pants build system [4] to have a toolchain which spits out single-file python executables with baked-in venv and portable across mainstream OSes with native (or close enough) performance. 0 - https://news.ycombinator.com/item?id=40040342 2 - https://shiv.readthedocs.io/en/latest/ 3 -... - Source: Hacker News / 10 months ago
  • Playable Sandbox Now Available
    Normally games made with pygame are not playable from the web. They can only be run from the command line or use PyInstaller or cx_Freeze to create a standalone executable. - Source: dev.to / over 1 year ago
  • Python GUIs
    I have found PyInstaller [1] to work well for packaging everything into a single ZIP file that unzips to a folder with an executable binary and all accompanying files (or even a single EXE file that self-extracts when run, but that increases startup time). It knows how to package PyQt and its associated Qt libraries (or PySide, which I actually prefer) so that they can be shipped with your application. [1... - Source: Hacker News / almost 2 years ago
  • Advice on turning tcod python game into something I can share with others?
    PyInstaller is the main way to build a Python executable. I'd recommenced bundling your program in the default one-folder mode and uploading it to Itch. Source: about 2 years ago
  • What's the best way to ship a Python script?
    There are tools, not from Python Software Foundation (or officially supported by them), such as Pyinstaller, that will try to produce a single executable file that you can distribute for people to install. Of course, this would depend on the controls on the end user devices allowing such an installation. There can be some compatibility challenges, but if you are using reasonably standard Python it shall probably... Source: about 2 years ago
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What are some alternatives?

When comparing Scikit-learn and PyInstaller, 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.

cx_Freeze - cx_Freeze is a set of scripts and modules for freezing Python scripts into executables in much the...

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

nuitka - Nuitka is a Python compiler.

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

Inno Setup - Inno Setup is a free installer for Windows programs.

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