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Scikit-learn VS WebStorm

Compare Scikit-learn VS WebStorm 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.

WebStorm logo WebStorm

The smartest JavaScript IDE
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • WebStorm Landing page
    Landing page //
    2023-07-20

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.

WebStorm features and specs

  • Intelligent Code Completion
    WebStorm offers smart code completion for JavaScript, HTML, CSS, and other languages, which helps developers write code faster and with fewer errors.
  • Built-in Developer Tools
    Integrated tools like a debugger, terminal, and VCS (Version Control System) support streamline the development process within one IDE.
  • Framework Support
    WebStorm provides out-of-the-box support for a wide variety of popular frameworks such as Angular, React, and Vue.js, making it flexible for modern web development.
  • Cross-platform
    WebStorm is available on Windows, macOS, and Linux, allowing developers to use it regardless of their operating system.
  • Customizable
    The IDE is highly customizable, allowing users to tailor the environment to meet their specific needs through plugins and settings.

Possible disadvantages of WebStorm

  • Cost
    WebStorm is a paid product, which may not be feasible for individual developers or small teams without a budget for tools.
  • Resource Intensive
    WebStorm can consume significant system resources, which might slow down your computer, especially if it's not high-spec.
  • Learning Curve
    For beginners, the wide array of features and settings can be overwhelming and may require a steep learning curve.
  • Occasional Performance Issues
    Users have reported occasional performance lags and glitches, especially when working with larger projects.
  • Updates and Compatibility
    Frequent updates might be a hassle for some users, and there can be compatibility issues with certain plugins after an update.

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 WebStorm

Overall verdict

  • Yes, WebStorm is considered a highly effective IDE for web development, praised for its robust feature set, ease of use, and overall efficiency.

Why this product is good

  • WebStorm is recognized for its advanced support for JavaScript, TypeScript, and other web technologies. It offers a wide range of features such as intelligent code completion, real-time code collaboration, and extensive plugin integrations, which enhance productivity and streamline the development process.

Recommended for

  • Front-end developers using JavaScript and TypeScript
  • Developers working with frameworks like React, Angular, or Vue.js
  • Teams seeking powerful collaboration tools
  • Developers looking for an IDE with strong debugging capabilities

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

WebStorm videos

+ Add

JetBrains WebStorm Review

More videos:

  • Review - Webstorm Best IDE For Javascript and Web Development
  • Review - What's New in WebStorm 2020.1
  • Review - VS Code vs Webstorm - 5 Things You NEED to Know!
  • Review - Why I prefer an IDE like WebStorm to a code editor like VS Code
  • Review - VSCode vs Webstorm - Which is Better for Developers? (A Detailed Comparison)

Category Popularity

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

Scikit-learn

WebStorm

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
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Text Editors
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Text Editors
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
IDE
0 0%
IDE
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 WebStorm

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

WebStorm Reviews

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

Based on our record, Scikit-learn seems to be more popular. 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 / 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 / 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 / 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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WebStorm mentions (0)

We have not tracked any mentions of WebStorm yet. Tracking of WebStorm recommendations started around Mar 2021.

What are some alternatives?

When comparing Scikit-learn and WebStorm, 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.

Sublime Text - Sublime Text is a sophisticated text editor for code, html and prose - any kind of text file. You'll love the slick user interface and extraordinary features. Fully customizable with macros, and syntax highlighting for most major languages.

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

Netbeans - NetBeans IDE 7.0. Develop desktop, mobile and web applications with Java, PHP, C/C++ and more. Runs on Windows, Linux, Mac OS X and Solaris. NetBeans IDE is open-source and free.

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

IntelliJ IDEA - Capable and Ergonomic IDE for JVM

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