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Scikit-learn VS yEd

Compare Scikit-learn VS yEd 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.

yEd logo yEd

yEd is a free desktop application to quickly create, import, edit, and automatically arrange diagrams. It runs on Windows, Mac OS X, and Unix/Linux.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • yEd 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.

yEd features and specs

  • User-Friendly Interface
    yEd offers a clean, intuitive interface that makes it easy for users to get started and create diagrams without a steep learning curve.
  • Versatile Diagram Types
    The software supports a wide range of diagram types including flowcharts, UML diagrams, network diagrams, and more, making it versatile for different needs.
  • Automatic Layouts
    yEd provides several powerful automatic layout algorithms that can quickly arrange complex diagrams into clear structures.
  • Cross-Platform
    yEd is compatible with multiple operating systems such as Windows, macOS, and Linux, providing flexibility for users across different platforms.
  • Free to Use
    yEd is free to download and use, which makes it an attractive option for individuals and organizations with budget constraints.

Possible disadvantages of yEd

  • Limited Collaboration Features
    yEd lacks built-in real-time collaboration features, which can be a disadvantage for teams needing to work simultaneously on the same diagram.
  • No Mobile Version
    There is no mobile version of yEd, which limits its usability for users who prefer creating diagrams on tablets or smartphones.
  • Steep Learning Curve for Advanced Features
    While the basic features are user-friendly, some of the more advanced functionalities can have a steep learning curve and may require time to master.
  • Limited Integration Options
    yEd does not offer extensive integration options with other productivity tools or software, which can be a drawback for users looking for a more connected workflow.
  • Occasional Performance Issues
    Users have reported occasional performance issues, especially when dealing with very large and complex diagrams.

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 yEd

Overall verdict

  • yEd is a good choice for users looking for a robust and versatile diagramming solution. Its free availability and rich features make it a strong contender among diagramming tools.

Why this product is good

  • yEd is considered a powerful diagramming tool because it offers an extensive range of features like automatic layout algorithms, various diagram types, easy-to-use interface, and cross-platform compatibility. It is especially appreciated for its ability to handle large data sets and produce clear, understandable visual representations quickly.

Recommended for

  • Business professionals who need to create organizational charts or flowcharts
  • Software developers who design complex system architectures
  • Researchers and analysts visualizing large data sets
  • Educators preparing educational materials
  • Students managing complex information for projects

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

yEd videos

+ Add

yEd Graph Editor in 90 seconds

More videos:

  • Tutorial - yED Graph Editor Tutorial - Make flowcharts, trees, graph Freeware.

Category Popularity

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

Scikit-learn

yEd

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Diagrams
0 0%
Diagrams
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Flowcharts
0 0%
Flowcharts
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 yEd

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

yEd Reviews

Best 7 Free Online XMind Alternatives for Windows
Another excellent tool that you can use for common and complex visual illustration is yEd. This aims to help users in terms of creating diagrams like UML, flowcharts, network diagram, org chart, and other process illustrations. With this XMind free alternative, you will find every icon and symbol you need for the aforementioned diagrams. On the other hand, users are...
Source: gitmind.com
40 Open Source, Free and Top Unified Modeling Language (UML) Tools
yEd is a desktop application that can be used to quickly and effectively generate high-quality diagrams. Users can create diagrams manually, or import their external data for analysis. yEd’s automatic layout algorithms arranges even large data sets with just the press of a button. yEd is freely available and runs on all major platforms: Windows, Unix/Linux, and Mac OS X.With...
Source: www.predictiveanalyticstoday.com

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 / 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 / almost 2 years ago
View more

yEd mentions (0)

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

What are some alternatives?

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

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

draw.io - Online diagramming application

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

LucidChart - LucidChart is the missing link in online productivity suites. LucidChart allows users to create, collaborate on, and publish attractive flowcharts and other diagrams from a web browser.

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

PlantUML - PlantUML is an open-source tool that uses simple textual descriptions to draw UML diagrams.

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