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Scikit-learn VS Stylus

Compare Scikit-learn VS Stylus 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.

Stylus logo Stylus

EXPRESSIVE, DYNAMIC, ROBUST CSS
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Stylus Landing page
    Landing page //
    2023-03-20

Stylus is a revolutionary new language, providing an efficient, dynamic, and expressive way to generate CSS. Supporting both an indented syntax and regular CSS style.

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.

Stylus features and specs

  • Simplified Syntax
    Stylus provides an optional semicolon-free and curly-brace-free syntax, making the code cleaner and easier to write.
  • Extensive Feature Set
    Stylus offers a wide range of features like mixins, nesting, variables, functions, and built-in functions, which increase its flexibility and power.
  • Preprocessor Enhancements
    Stylus includes advanced features that are not available in CSS alone, such as mathematical operations, conditionals, and loops, which can make stylesheets more dynamic and maintainable.
  • JavaScript Interoperability
    Stylus allows embedding of JavaScript expressions and logic directly within the stylesheets, providing developers with additional functionality and control.

Possible disadvantages of Stylus

  • Learning Curve
    The flexibility and multitude of features in Stylus can introduce complexity, making it harder for beginners to grasp quickly compared to more straightforward CSS preprocessors.
  • Less Popularity
    Stylus is less popular than other preprocessors like Sass or LESS, which might result in fewer learning resources, community support, and third-party tools.
  • Potential for Overuse
    The advanced features could lead developers to overuse them, resulting in overly complex code that is difficult to maintain and understand.
  • Build Tool Dependencies
    Integration of Stylus into a project generally requires additional build tools and configurations, which can add to the setup and maintenance overhead.

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.

Stylus videos

+ Add

Best stylus for iPhone! Don't waste your money!

More videos:

  • Review - What is the best iPad stylus?
  • Review - Review: MEKO 2-in-1 Stylus (2nd Gen)

Category Popularity

0-100% (relative to Scikit-learn and Stylus)

Scikit-learn

Stylus

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Developer Tools
0 0%
Developer Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Browser Extension
0 0%
Browser Extension
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 Stylus

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

Stylus Reviews

112 Best Chrome Extensions You Should Try (2021 List)
Stylus offers functionality to install and write themes. It is an open-source community-driven extension that makes it much better than its counterparts. Using Stylus, you can install themes from popular repositories, backup your installed styles, and much more. Watch the above video to learn more about Stylus.
Source: www.einsstark.tech

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Stylus. 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 / 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 / about 1 year 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 / about 2 years ago
View more

Stylus mentions (14)

  • 100+ Must-Have Web Development Resources
    Stylus: Provides a more efficient and elegant way to generate CSS. - Source: dev.to / 8 months ago
  • 33 front-end development tools developers use in 2024
    Sass, Less and Stylus, extends CSS by adding variables, nesting mixins, and other features. It's an excellent solution for organizing huge and complex stylesheets. - Source: dev.to / 12 months ago
  • BEM Modifiers in Pure CSS Nesting
    I hate preprocessors. Be it SASS, SCSS, LESS, Stylus, or any other. Really, without any exceptions. Though, I think my hatred for preprocessors is not because of the technology itself, but because of how other people use them. Throughout my development career, I have often encountered tickets where a seemingly simple task, like changing the text size, which should take minutes, ended up taking me hours. This is... - Source: dev.to / about 1 year ago
  • Future of CSS: Functions and Mixins
    Traditionally CSS lacked features such as variables, nesting, mixins, and functions. This was frustrating for Developers as it often led to CSS quickly becoming complex and cumbersome. In an attempt to make code easier and less repetitive CSS pre-processors were born. You would write CSS in the format the pre-processor understood and, at build time, you'd have some nice CSS. The most common pre-processors these... - Source: dev.to / over 1 year ago
  • Quick Guide To CSS Preprocessors
    The Stylus is built on Node.js. It differs from Sass and Less, which are more opinionated to the syntax; the stylus allows you to omit semicolons, colons, and braces if you want at any time. Another cool feature is that the stylus has a property lookup feature. You can do that easily if you set property X relative to property Y's value. The stylus can be more concise because of its flexibility, but it depends on... - Source: dev.to / over 2 years ago
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What are some alternatives?

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

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

Sass - Syntatically Awesome Style Sheets

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

Dark Reader - Reduce eye strain in your browser with this extension that provides a dark theme for browsing.

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

PostCSS - Increase code readability. Add vendor prefixes to CSS rules using values from Can I Use. Autoprefixer will use the data based on current browser popularity and property support to apply prefixes for you.

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