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Scikit-learn VS Standard JS

Compare Scikit-learn VS Standard JS 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.

Standard JS logo Standard JS

DevOps, Build, Test, Deploy, and Code Review
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Standard JS Landing page
    Landing page //
    2023-08-29

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.

Standard JS features and specs

  • Zero Configuration
    Standard JS comes with a set of rules and configurations out of the box. This eliminates the need to set up a linting configuration file, saving developers time and reducing the cognitive load associated with decision-making.
  • Uniformity
    By enforcing a consistent style across projects, Standard JS helps to create a uniform codebase. This makes it easier for teams to read and understand each other's code, reducing onboarding time for new developers.
  • Community and Support
    As a popular style guide and linter, Standard JS has a robust community and extensive documentation. This support makes it easier for developers to find solutions to issues and to integrate Standard JS into their projects.
  • Less Distraction
    With pre-set rules, developers spend less time debating over coding styles and more time focusing on actual code logic and building functionality.

Possible disadvantages of Standard JS

  • Limited Customization
    Since Standard JS comes with a predefined set of rules, it offers limited flexibility for customization. Developers who prefer tailor-made configurations might find it restrictive.
  • Opinionated Rules
    Standard JS follows an opinionated approach to styling, which might not align with certain team or individual preferences. Some developers might find specific enforced styles disagreeable.
  • Compatibility Issues
    In some cases, Standard JS rules might conflict with pre-existing project configurations or other linters in the project, possibly causing friction during integration.
  • Learning Curve
    For developers new to Standard JS, there may be a learning curve as they acclimate to its specific rules and enforcement practices, particularly if they're used to other style guides.

Analysis of Scikit-learn

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Standard JS videos

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Category Popularity

0-100% (relative to Scikit-learn and Standard JS)

Scikit-learn

Standard JS

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Code Coverage
0 0%
Code Coverage
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Code Analysis
0 0%
Code Analysis
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 Standard JS

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

Standard JS Reviews

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Social recommendations and mentions

Scikit-learn might be a bit more popular than Standard JS. We know about 31 links to it since March 2021 and only 27 links to Standard JS. 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 / 4 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 / 6 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 / 12 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 / over 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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Standard JS mentions (27)

  • Mastering Code Quality: Setting Up ESLint with Standard JS in TypeScript Projects
    Sorry, I've gone too far. I'm not here to persuade you to use Standard JS. My intention is to provide information and guidance on configuring JavaScript Standard Style for your team, should you agree with me or have other reasons to choose it. - Source: dev.to / about 1 year ago
  • Why is Prettier rock solid?
    I picked up standard[1] a while back for this reason, I don't want to have to think about it. It works fine, I have no complaints (took me a while to get used to not using semi-colons but now I prefer it) Same reason I use `cargo fmt` as well. [1] https://standardjs.com/. - Source: Hacker News / over 1 year ago
  • My prepared repositories for hacktoberfest 23 - any contributions are welcomed 🚀
    A Thin JavaScript Document Storage with Middleware Stack. - Source: dev.to / over 1 year ago
  • Dumb question
    For example, if you use https://standardjs.com/ - it will error on your second code snippet and if you ask it for an autofix - it will transfer the minus sign to the first line. Source: over 2 years ago
  • Unleash the Power of Java: A JavaScript Developer's Guide to Best Practices in Java Development
    In comparison, JavaScript doesn't have a strict coding standard, although it does have widely accepted code style guides like the Airbnb JavaScript Style Guide and the JavaScript Standard Style. These guides provide recommendations for code formatting and naming conventions, but they are not as strictly enforced as the Java coding standard. - Source: dev.to / over 2 years ago
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What are some alternatives?

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

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

Prettier - An opinionated code formatter

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

ESLint - The fully pluggable JavaScript code quality tool

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

EditorConfig - EditorConfig is a file format and collection of text editor plugins for maintaining consistent coding styles between different editors and IDEs.

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