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Scikit-learn VS Prettier

Compare Scikit-learn VS Prettier 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.

Prettier logo Prettier

An opinionated code formatter
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Prettier Landing page
    Landing page //
    2022-06-27

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.

Prettier features and specs

  • Consistency
    Ensures a uniform code style across different files and projects, reducing code review conflicts and making it easier for team members to work on the same codebase.
  • Time-saving
    Automates code formatting, which saves developers time that they would otherwise spend on manually formatting code.
  • Integrations
    Works well with various code editors, IDEs, and continuous integration tools, making it easy to integrate into existing workflows.
  • Language Support
    Supports a wide range of programming languages and file types beyond JavaScript, including TypeScript, CSS, HTML, Markdown, JSON, and more.
  • Community and Documentation
    Backed by a strong community and comprehensive documentation that provide quick solutions and guide you through setup and customization.

Possible disadvantages of Prettier

  • Lack of Customization
    Prettier enforces a specific set of rules and offers limited customization options compared to other linters or formatters, which may not satisfy all coding style preferences.
  • Learning Curve
    New users may face a learning curve when configuring and integrating Prettier into their existing workflow, especially if they are not familiar with code formatters.
  • Performance Overhead
    Running Prettier on large projects can introduce performance overhead, particularly during automated tasks like pre-commit hooks or continuous integration processes.
  • Conflict with Existing Tools
    May conflict with other code linters and formatters, requiring additional configuration to ensure compatibility and avoid duplicated efforts.

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.

Analysis of Prettier

Overall verdict

  • Yes, Prettier is generally considered a good tool because of its ease of use, ability to enforce a consistent coding style, and its support for various programming languages. It is highly valued in teams looking to streamline their code format and improve teamwork by reducing stylistic debates.

Why this product is good

  • Prettier is a widely used code formatter that helps maintain consistent code style across a project. It automatically formats code to adhere to a set of rules, reducing time spent on code reviews and making the codebase more readable and maintainable. Its integration with various editors and support for multiple languages enhance its utility in diverse development environments.

Recommended for

  • Teams seeking to maintain a consistent code style across members
  • Developers who want to automate code styling tasks
  • Projects that benefit from reducing time spent on stylistic feedback in code reviews
  • Individuals who appreciate the integration of code formatting tools within their development environment

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Prettier videos

+ Add

Code Formatting with Prettier in Visual Studio Code

More videos:

  • Review - ESLint + Prettier + VS Code — The Perfect Setup
  • Review - Miranda Lambert -- Only Prettier [REVIEW/RATING]

Category Popularity

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

Scikit-learn

Prettier

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 Prettier

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

Prettier Reviews

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

Based on our record, Prettier should be more popular than Scikit-learn. It has been mentiond 289 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 / 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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Prettier mentions (289)

  • A Case for Semicolon-less JavaScript (ASI)
    In short semi-columns in JavaScript helps reduce the surface for bugs in poorly maintained code bases, and provides clearer intent to formatters such as prettier. - Source: dev.to / 3 months ago
  • Should you stop using prettier? Maybe
    For years, I've been prettier's biggest fan... Introducing it to every codebase at a new role, instantly adding it to any new repository, installing additional plugins such as tailwind or xml and of course, ensuring the "Format on save" option is switched on. - Source: dev.to / 23 days ago
  • 🚀 Biome Has Entered the Chat: A New Tool to Replace ESLint and Prettier
    If you’ve ever set up a JavaScript or TypeScript project, chances are you've spent way too much time configuring ESLint, Prettier, and their dozens of plugins. We’ve all been there — fiddling with .eslintrc, fighting with formatting conflicts, and installing what feels like half the npm registry just to get decent code quality tooling. - Source: dev.to / 2 months ago
  • Mastering JavaScript Syntax with the Help of AI
    Use tools like Prettier to reformat code when things get messy. - Source: dev.to / 3 months ago
  • Matanuska ADR 017 - Vitest, Vite, Grabthar, Oh My!
    Unfortunately, this did mean that configuration began to sprawl. At this point, I had configurations not just for Vite (shared with Vitest) and tsc, but also for Prettier, ESLint and even ShellCheck. Many of these files had shared settings that needed to match each other. This was somewhat manageable, until Vite was also in the mix. - Source: dev.to / 6 months ago
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What are some alternatives?

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

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

ESLint - The fully pluggable JavaScript code quality tool

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

Tailwind CSS - A utility-first CSS framework for rapidly building custom user interfaces.

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

Next.js - A small framework for server-rendered universal JavaScript apps

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