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V (programming language) VS Scikit-learn

Compare V (programming language) VS Scikit-learn and see what are their differences

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V (programming language) logo V (programming language)

Simple, fast, safe, compiled language for developing maintainable software.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • V (programming language) Landing page
    Landing page //
    2022-11-25
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

V (programming language) features and specs

  • Fast Compilation
    V is designed to compile extremely fast, typically within less than a second, regardless of the size of the codebase.
  • Simplicity
    The syntax is simple and easy to learn, drawing inspiration from languages like Go and Python which reduces the learning curve for new developers.
  • Performance
    V aims to offer high performance akin to C, enabling developers to write highly efficient programs.
  • Safe Programming
    It includes built-in mechanisms to avoid common bugs and aims to provide safety features like immutable data structures and option types.
  • Single Binary
    V produces a single small binary without external dependencies, making distribution straightforward and more secure.
  • Cross-Platform
    V supports cross-compilation out of the box, allowing developers to build applications for multiple operating systems from a single codebase.

Possible disadvantages of V (programming language)

  • Maturing Ecosystem
    As a relatively new language, V's ecosystem, including libraries, frameworks, and community support, is still growing and may not be as mature as more established languages.
  • Limited Tooling
    Compared to more established languages, tools like IDE support, debuggers, and other development utilities are still limited or in early stages.
  • Smaller Community
    The community around V is smaller, which can result in fewer resources, tutorials, and third-party libraries being available.
  • Learning Curve for Advanced Features
    While the syntax is simple, mastering some of V's advanced features and paradigms may pose a learning curve to developers coming from more mainstream languages.
  • Rapid Changes
    As the language is still evolving, there can be rapid changes and updates which may introduce breaking changes or require frequent revisions of codebases.

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.

V (programming language) videos

+ Add

V Programming Language (Vlang): First Impression - Mike Shah

More videos:

  • Review - Introduction to V and its features (Sydney Computing Society)
  • Review - Testing the NEW "V" programming language!
  • Demo - Presentation of Vlang at IBM
  • Review - An introduction to V (Vlang)

Scikit-learn videos

+ Add

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 V (programming language) and Scikit-learn)

V (programming language)

Scikit-learn

Programming Language
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Programming Language
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Data Science And Machine Learning
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Data Science And Machine Learning
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OOP
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OOP
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Data Science Tools
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Data Science Tools
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User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare V (programming language) and Scikit-learn

V (programming language) Reviews

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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

Social recommendations and mentions

Based on our record, V (programming language) should be more popular than Scikit-learn. It has been mentiond 74 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.

V (programming language) mentions (74)

  • Koto Programming Language
    > For me the biggest gap in programming languages is a rust like language with a garbage collector, instead of a borrow checker. https://vlang.io. - Source: Hacker News / about 2 months ago
  • Go Is a Well-Designed Language
    I think V [1] is what Go should’ve been. Simple, compiles fast, integrated language tooling, in fact quite similar to Go, but without all the dumb design decisions. Unlike Go, it has sum types, enums, immutable-by-default variables, option/result types, various other goodies and the syntax for for loops actually makes sense. It’s a shame that the compiler is quite buggy, but hopefully that’s going to improve. [1]... - Source: Hacker News / 4 months ago
  • Mantis - new file log driver
    Mantis is a type-safe web framework written in V that emphasizes explicit, magic-free code. - Source: dev.to / 5 months ago
  • Mantis, a web framework written in V
    For development, V offers both an interpreter and watch mode, combining the convenience of scripting languages with the type safety and performance of compiled languages. It even includes built-in channel-compatible concurrency - truly the best of both worlds. - Source: dev.to / 5 months ago
  • Lies we tell ourselves to keep using Golang
    What is quite interesting (after looking at their documentation), is that V lang[1] has all that is mentioned: `?`[2], `or`[2], sum types[4], and can return multiple values[5]. [1]: https://vlang.io/ [2]: https://github.com/vlang/v/blob/master/doc/docs.md#optionresult-types-and-error-handling [4]: - Source: Hacker News / 6 months ago
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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
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What are some alternatives?

When comparing V (programming language) and Scikit-learn, you can also consider the following products

Nim (programming language) - The Nim programming language is a concise, fast programming language that compiles to C, C++ and JavaScript.

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

Go Programming Language - Go, also called golang, is a programming language initially developed at Google in 2007 by Robert...

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

D (Programming Language) - D is a language with C-like syntax and static typing.

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

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