Software Alternatives, Accelerators & Startups

GDevelop VS Scikit-learn

Compare GDevelop VS Scikit-learn and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

GDevelop logo GDevelop

GDevelop is an open-source game making software designed to be used by everyone.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • GDevelop Landing page
    Landing page //
    2023-10-23
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

GDevelop features and specs

  • User-Friendly Interface
    GDevelop provides a drag-and-drop interface, making it accessible for beginners who don't have prior coding experience.
  • Cross-Platform Export
    Games created with GDevelop can be exported to multiple platforms, including Windows, macOS, Linux, Android, iOS, and the web.
  • Free and Open Source
    GDevelop is completely free and its source code is open for anyone to modify and improve.
  • Extensive Documentation
    The platform provides a wide range of tutorials, examples, and thorough documentation, making it easier for developers to learn and utilize the tool.
  • Vibrant Community
    An active community forum and resources are available, providing support and opportunities for collaboration.
  • No-Code Solution
    GDevelop allows game creation without any coding, making it highly suitable for rapid prototyping and educational purposes.

Possible disadvantages of GDevelop

  • Performance Limitations
    The engine may struggle with performance issues for more complex games, especially those with high-end graphics and intensive computations.
  • Limited Advanced Features
    While suitable for 2D game development, GDevelop lacks advanced features found in other engines, potentially limiting more experienced developers.
  • Learning Curve for Advanced Usage
    Although easy for beginners, mastering the platform for more complex projects can have a steep learning curve.
  • Limited Integration
    Integration with third-party tools and services is not as extensive as in some other, more established game development engines.
  • Project Collaboration
    Collaborative features are relatively basic, potentially making it less ideal for larger, team-based projects.
  • 2D Only
    GDevelop focuses exclusively on 2D game development, which can be a downside for those looking to develop 3D games.

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.

GDevelop videos

GDevelop 5 -- Ultimate Beginner Game Engine?

More videos:

  • Review - Clickteam Fusion 2.5 Vs GDevelop 5 - (Game Engine REVIEW 2019 )
  • Review - Clickteam Fusion 2.5 Vs GDevelop 5 - (Game Engine REVIEW 2020 )
  • Tutorial - Beginner Multiplayer Tutorial

Scikit-learn videos

Learning Scikit-Learn (AI Adventures)

More videos:

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

Category Popularity

0-100% (relative to GDevelop and Scikit-learn)
Game Development
100 100%
0% 0
Data Science And Machine Learning
Game Engine
100 100%
0% 0
Data Science Tools
0 0%
100% 100

User comments

Share your experience with using GDevelop and Scikit-learn. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare GDevelop and Scikit-learn

GDevelop Reviews

16 Scratch Alternatives
Beginners who don’t have any programming skills but still want to create some games can quickly access one of the best platforms based on the open source network to help them develop games named the GDevelop. This platform lets users release their creative skills to quickly build games, such as puzzles, shoot-em-ups, strategy, racing, adventure, and more. It can even permit...
20 Best Scratch Alternatives 2023
GDevelop is described as a “free and easy game-making app.” It’s similar to Scratch in that it’s a no-code platform; it doesn’t require using programming languages. GDevelop is also free and open source.
Trending 10 BEST Video Game Design & Development Software 2021
Open-source free software, GDevelop allows developers to make games without programming skills. It allows you to create objects for games such as sprites, text objects, video objects, and custom shapes.
Best Game Engines for Linux in 2021
Construct 3 is free with limits. After that, you have to sign up for a monthly subscription. If you can not afford to pay for it, you can use GDevelop, an alternative to Construct 3 for Linux.
Source: kerneltips.com
Trending 7 Best Game Development Software 2021
GDevelop is the best game making software for beginners & professionals. GDevelop provides you easy and simplistic interface, which most developers like in GDevelop.
Source: vilesolid.com

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...

Social recommendations and mentions

Based on our record, GDevelop should be more popular than Scikit-learn. It has been mentiond 78 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.

GDevelop mentions (78)

  • No-Code Game Development: Using AI to Build Your First Game
    GDevelop combines open-source flexibility with powerful no-code features. Their recent AI plugins provide remarkable capabilities:. - Source: dev.to / 4 days ago
  • Ask HN: Platform for 11 year old to create video games?
    Humble Bundle has a Godot bundle is available for the next day or so. That might be a good one to look at if you're ok with leaning into code a bit (gdscript is very very similar to python). https://www.humblebundle.com/software/learn-godot-43-complete-course-bundle-software Also check out the RPG Maker bundle. That's pretty point-and-click. You can have something basic up and running in a couple minutes... - Source: Hacker News / 8 months ago
  • Exploring Raylib and Open Source
    I selected this library as I normally use much higher-level tools to develop games such as p5.js, or GDevelop. Both these tools are amazing in their own right; however, I want to learn how these processes operate on a much lower level. These tools take care of a lot of issues for you ranging from asset to memory management. Raylib is still cross-platform but does not handle these tasks for the programmer which I... - Source: dev.to / 8 months ago
  • Unity’s New Pricing: A Wake-Up Call on the Importance of Open Source in Gaming
    It's not as monolithic as you'd think. There are lots of engines out there but their communities aren't very vocal compared to Unity, Unreal, and especially Godot's community. Take a look at: https://itch.io/game-development/engines/most-projects And https://www.gamedeveloper.com/blogs/the-generous-space-of-alternative-game-engines-a-curation- If you look at both of these you'll see just how many engines there are... - Source: Hacker News / over 1 year ago
  • Ask HN: Favorite Game Engine?
    I'm not really a game maker, but would like to give a shout out to the fabulous https://gdevelop.io/ It has everything you need, is free and its VISUAL PROGRAMMING is fab... - Source: Hacker News / over 1 year ago
View more

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 / 3 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 / 11 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
View more

What are some alternatives?

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

Godot Engine - Feature-packed 2D and 3D open source game engine.

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

Unity - The multiplatform game creation tools for everyone.

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

Unreal Engine - Unreal Engine 4 is a suite of integrated tools for game developers to design and build games, simulations, and visualizations.

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