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Scikit-learn VS F#

Compare Scikit-learn VS F# and see what are their differences

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  3. » Reviews
  4. » Alternatives

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

F# logo F#

F# is a mature, open source, cross-platform, functional-first programming language.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • F# Landing page
    Landing page //
    2021-09-15

We recommend LibHunt F# for discovery and comparisons of trending F# projects.

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.

F# features and specs

  • Functional Programming Paradigm
    F# primarily supports functional programming, which promotes immutability and first-class functions, leading to more predictable and maintainable code.
  • Interoperability
    F# provides seamless interoperability with .NET libraries and languages like C#, allowing developers to leverage a vast ecosystem of tools and libraries.
  • Conciseness
    F# code tends to be concise and expressive, reducing boilerplate code and enhancing readability.
  • Type Inference
    Powerful type inference capabilities reduce the need for explicit type annotations, making the code easier to write and refactor.
  • Asynchronous Programming
    F# provides robust support for asynchronous programming, enabling the creation of responsive applications and efficient I/O handling.
  • Community and Resources
    An active community and wealth of online resources provide support and facilitate learning through forums, tutorials, and documentation.
  • Multi-Paradigm
    Despite its functional core, F# also supports imperative and object-oriented programming, offering flexibility to developers.

Possible disadvantages of F#

  • Learning Curve
    For developers coming from imperative or object-oriented backgrounds, the functional programming paradigm in F# can present a steep learning curve.
  • IDE and Tooling
    Although F# is integrated into Visual Studio, the overall tooling and IDE support for F# is not as mature as for more established languages like C#.
  • Market Demand
    The demand for F# skillsets in the job market is comparatively lower than for more mainstream languages, potentially affecting career opportunities.
  • Performance Overhead
    While generally efficient, certain operations in F# may incur performance overhead due to the functional aspects and abstractions, especially when not optimized.
  • Library Support
    Although F# can access the .NET library ecosystem, it has a relatively smaller number of libraries and frameworks specifically designed for it compared to languages like Python or JavaScript.
  • Niche Language
    F# is often considered a niche language, which can lead to a smaller community and fewer resources compared to more popular languages.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

F# videos

+ Add

F# Software Foundation Year in Review

More videos:

  • Review - F# Blues Harp Review
  • Review - F# base Bhavika flute review by Dhyey patel ji

Category Popularity

0-100% (relative to Scikit-learn and F#)

Scikit-learn

F#

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Programming Language
0 0%
Programming Language
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
OOP
0 0%
OOP
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 F#

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

F# Reviews

We have no reviews of F# yet.
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Social recommendations and mentions

Scikit-learn might be a bit more popular than F#. We know about 31 links to it since March 2021 and only 21 links to F#. 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 / 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

F# mentions (21)

  • What's New in F# 9
    It's an open-source project with its own F# Software Foundation. If Microsoft drops it, I think it would continue. https://fsharp.org/. - Source: Hacker News / 6 months ago
  • Rust panics under the hood, and implementing them in .NET
    Before Rich made Clojure for the JVM, he wrote dotLisp[1] for the CLR. Not long after Clojure was JVM hosted, it was also CLR hosted[2]. One of my first experiences with ML was F#[3], a ML variant that targets the CLR. These all predate the MIT licensed .net, but prior to that there was mono, which was also MIT licensed. 1: https://dotlisp.sourceforge.net/dotlisp.htm 2: https://github.com/clojure/clojure-clr. - Source: Hacker News / 8 months ago
  • Roc – A fast, friendly, functional language
    Oh yeah. A key hindrance of F# is that MS treats it like a side project even though it's probably their secret weapon, and a lot of the adopters are dotnet coders who already know the basics so the on-boarding is less than ideal. https://fsharp.org/ is the best place to actually start. https://fsharpforfunandprofit.com/ is the standard recommendation from there but there's finally some good youtube and other... - Source: Hacker News / over 1 year ago
  • Building React Components Using Unions in TypeScript
    Naturally I’d recommend using a better language such as ReScript or Elm or PureScript or F#‘s Fable + Elmish, but “React” is the king right now and people perceive TypeScript as “less risky” for jobs/hiring, so here we are. - Source: dev.to / over 1 year ago
  • I am a ChatGPT bot - Ask me anything #2
    Are you really a bot? Yes, I'm a small F# program that glues together the public API's provided by Reddit and OpenAI. I was created by /u/brianberns. You can find my source code here. Source: about 2 years ago
View more

What are some alternatives?

When comparing Scikit-learn and F#, 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.

Elixir - Dynamic, functional language designed for building scalable and maintainable applications

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

C++ - Has imperative, object-oriented and generic programming features, while also providing the facilities for low level memory manipulation

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

Clojure - Clojure is a dynamic, general-purpose programming language, combining the approachability and interactive development of a scripting language with an efficient and robust infrastructure for multithreaded programming.

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