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Scikit-learn VS Eva Design System

Compare Scikit-learn VS Eva Design System and see what are their differences

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  3. » Reviews
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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.

Eva Design System logo Eva Design System

A free customizable design system
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Eva Design System Landing page
    Landing page //
    2022-07-16

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.

Eva Design System features and specs

  • Customization Flexibility
    Eva Design System offers a high degree of customization, allowing developers and designers to easily adjust themes and components to match specific brand guidelines.
  • Comprehensive Component Library
    The system provides a wide range of pre-designed components, making it easier and faster to build applications with consistent design.
  • Open Source
    Being open-source, Eva Design System allows for community contributions and ensures that it can be freely used and shared, fostering a collaborative environment for improvement.
  • Styled with Theme Variables
    Eva uses theme variables extensively, which facilitates skinning and theming applications, ensuring a unified look and feel across different components and screens.
  • Built-in Dark Mode
    Eva Design System comes with built-in support for dark mode, making it easier for developers to implement this feature without additional complexity.

Possible disadvantages of Eva Design System

  • Learning Curve
    Due to its comprehensive nature and customization options, there may be a steep learning curve for new users who are unfamiliar with the system.
  • Documentation Gaps
    While it is generally well-documented, there can be occasional gaps in the documentation that may hinder smooth implementation or require users to seek additional help.
  • Dependency Management
    Managing dependencies can become complex, especially when integrating Eva Design System with other libraries or frameworks, potentially leading to conflicts or additional overhead.
  • Performance Overhead
    The extensive customization and theming options, while beneficial, may also introduce some performance overhead, particularly in large-scale applications.
  • Community Size
    Compared to more popular design systems, Eva's community is relatively smaller, which may result in fewer third-party integrations, plugins, or community-driven solutions.

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 Eva Design System

Overall verdict

  • Eva Design System is a highly recommended choice for teams looking for a modern and cohesive design language that can accelerate development and ensure design consistency across projects.

Why this product is good

  • Eva Design System is considered good because it offers a comprehensive and consistent framework for building user interfaces that are visually pleasing and user-friendly. It provides a customizable design language, a collection of reusable UI components, and detailed documentation that can help streamline the design and development process. Additionally, it is open-source, which allows for community contributions and makes it adaptable to various project needs.

Recommended for

    Eva Design System is ideal for UI/UX designers, front-end developers, and product teams who want a flexible and robust system for creating applications with a consistent look and feel. It is especially useful for those working on cross-platform projects, as it provides components and guidelines that work well across different environments.

Scikit-learn videos

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Learning Scikit-Learn (AI Adventures)

More videos:

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

Eva Design System videos

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Category Popularity

0-100% (relative to Scikit-learn and Eva Design System)

Scikit-learn

Eva Design System

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Design Tools
0 0%
Design Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Prototyping
0 0%
Prototyping
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 Eva Design System

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

Eva Design System Reviews

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Social recommendations and mentions

Based on our record, Scikit-learn seems to be a lot more popular than Eva Design System. While we know about 31 links to Scikit-learn, we've tracked only 1 mention of Eva Design System. 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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Eva Design System mentions (1)

  • Dark Mode for Developers
    Based on the EVA Design System, the Nebular library is one of the best UI libraries for Angular. Nebular provides inbuilt, customizable themes like the default theme, cosmic theme, dark theme, etc. The mobile version of Nebular, called UI Kitten, also supports the dark theme. - Source: dev.to / over 3 years ago

What are some alternatives?

When comparing Scikit-learn and Eva Design System, 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.

Ant Design System for Figma - A large library of 2100+ handcrafted UI components

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

Design Systems Repo - A collection of design system examples and resources

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

Fabrx.co - Create the UI Kits, Bootstrap 5 or HTML Dashboards that you need in minutes. Have some design sense to spare? Fabrx will help you tackle even the trickiest of projects.

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