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Scikit-learn VS ESLint

Compare Scikit-learn VS ESLint 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.

ESLint logo ESLint

The fully pluggable JavaScript code quality tool
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • ESLint Landing page
    Landing page //
    2022-09-14

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.

ESLint features and specs

  • Customization
    ESLint is highly customizable through configuration files, allowing developers to tailor the linting process to fit their specific coding standards and project requirements.
  • Extensibility
    With a wide range of plugins and the ability to write custom rules, ESLint can be extended to accommodate unique project needs or additional languages and frameworks.
  • Community Support
    ESLint has a large and active community, ensuring continuous improvement, frequent updates, and a wealth of shared knowledge and resources.
  • Integrations
    ESLint integrates seamlessly with most development environments, build tools, and version control systems, making it easy to incorporate into existing workflows.
  • Error Prevention
    By statically analyzing code to catch potential errors and bad practices before runtime, ESLint helps improve code quality and reduce bugs.
  • Consistency
    Applying ESLint across a project ensures coding standards are maintained consistently, which is particularly beneficial for teams with multiple developers.

Possible disadvantages of ESLint

  • Initial Setup
    Configuring ESLint for the first time can be daunting, especially for those who are new to the tool or have complex project requirements.
  • Performance
    On large codebases, ESLint can sometimes slow down builds or editor performance due to the extensive analysis it performs.
  • Learning Curve
    There is a learning curve associated with understanding and configuring ESLint rules, which can be challenging for beginners.
  • Strictness
    Depending on the configuration, ESLint can be very strict, leading to a large number of warnings or errors that may initially overwhelm developers not accustomed to such rigorous linting.
  • Opinionated Rules
    Some ESLint default rules may not align with every developer's or team's coding style preferences, necessitating further customization and adjustment.
  • Maintenance
    Keeping ESLint configurations and plugins up to date requires ongoing maintenance, especially as projects evolve and dependencies change.

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.

ESLint videos

+ Add

ESLint Quickstart - find errors automatically

More videos:

  • Review - ESLint + Prettier + VS Code — The Perfect Setup
  • Review - Linting and Formatting JavaScript with ESLint in Visual Studio Code

Category Popularity

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

Scikit-learn

ESLint

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 ESLint

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

ESLint Reviews

8 Best Static Code Analysis Tools For 2024
You can use ESLint through a supported IDE such as VS Code, Eclipse, and IntelliJ IDEA or integrate it with your CI pipelines. Moreover, you can install it locally using a package manager like npm, yarn, npx, etc.
Source: www.qodo.ai

Social recommendations and mentions

Based on our record, ESLint should be more popular than Scikit-learn. It has been mentiond 267 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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ESLint mentions (267)

  • Never lose valuable error context in JavaScript
    While ESLint is the go-to tool for code quality in JavaScript, it doesn’t provide any built-in rule for this. - Source: dev.to / 18 days ago
  • Shopify: Getting to grips with GraphQL
    This linting is designed to work with eslint, which is very commonly used in the JavaScript world. - Source: dev.to / 26 days ago
  • Most Effective Approaches for Debugging Applications
    Static code analysis tools scan code for potential issues before execution, catching bugs like null pointer dereferences or race conditions early. Daniel Vasilevski, Director and Owner of Bright Force Electrical, shares, “Utilizing static code analysis tools gives us a clear look at what’s going wrong before anything ever runs.” During a scheduling system rebuild, SonarQube flagged a concurrency flaw, preventing... - Source: dev.to / about 1 month ago
  • Static Code Analysis: Ensuring Code Quality Before Execution
    ESLint – Widely used for JavaScript/TypeScript projects to catch style and logic errors. - Source: dev.to / 2 months 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
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What are some alternatives?

When comparing Scikit-learn and ESLint, 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.

Prettier - An opinionated code formatter

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

SonarQube - SonarQube, a core component of the Sonar solution, is an open source, self-managed tool that systematically helps developers and organizations deliver Clean Code.

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

CodeClimate - Code Climate provides automated code review for your apps, letting you fix quality and security issues before they hit production. We check every commit, branch and pull request for changes in quality and potential vulnerabilities.

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