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Clojure VS Scikit-learn

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

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  2. » Videos
  3. » Reviews
  4. » Alternatives

Clojure logo 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.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Clojure Landing page
    Landing page //
    2023-09-19

We recommend LibHunt Clojure for discovery and comparisons of trending Clojure projects.

  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Clojure features and specs

  • Functional Programming Paradigm
    Clojure emphasizes immutability and first-class functions, which can lead to more predictable and maintainable code.
  • Interoperability with Java
    Clojure runs on the JVM, allowing seamless integration with the vast ecosystem of Java libraries and tools.
  • REPL Driven Development
    Clojure's Read-Eval-Print Loop (REPL) allows for interactive programming, making it easier to test and debug code in real time.
  • Concise Syntax
    Clojure's syntax is minimalistic and expressive, which can lead to more concise and readable code.
  • Concurrency Support
    Clojure provides strong support for concurrent programming with features like Software Transactional Memory (STM) and immutable data structures.

Possible disadvantages of Clojure

  • Steep Learning Curve
    The functional programming paradigm and Lisp-like syntax can be challenging for newcomers, particularly those from imperative programming backgrounds.
  • Performance Overhead
    Clojure's emphasis on immutability can introduce performance overhead compared to languages that use mutable data structures.
  • Limited Tooling
    While improving, the ecosystem for Clojure is not as mature as for some other mainstream languages, which can pose challenges in finding robust development and debugging tools.
  • Less Mainstream
    Clojure is not as commonly used as languages like Python or Java, which can make it harder to find experienced developers or community support.
  • Verbose Error Messages
    Error messages in Clojure can sometimes be verbose and difficult to understand, which can complicate the debugging process.

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.

Clojure videos

+ Add

What is the business value of Clojure?

More videos:

  • Review - Blog in Clojure Code Review
  • Review - Clojure Web App Code Review

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 Clojure and Scikit-learn)

Clojure

Scikit-learn

Programming Language
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Programming Language
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Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
OOP
100 100%
OOP
0% 0
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 Clojure and Scikit-learn

Clojure 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

Clojure might be a bit more popular than Scikit-learn. We know about 39 links to it since March 2021 and only 31 links to Scikit-learn. 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.

Clojure mentions (39)

  • Create a Server Driven CLI from your REST API
    Another project of mine Bob can be seen as an example of spec-first design. All its tooling follow that idea and its CLI inspired Climate. A lot of Bob uses Clojure a language that I cherish and who's ideas make me think better in every other place too. - Source: dev.to / 3 months ago
  • Scheming About Clojure
    Clojure is a LISP for the Java Virtual Machine (JVM). As a schemer, I wondered if I should give Clojure a go professionally. After all, I enjoy Rich Hickey's talks and even Uncle Bob is a Clojure fan. So I considered strength and weaknesses from my point of view:. - Source: dev.to / 6 months ago
  • Moving your bugs forward in time
    ‍For the rest of this post I’ll list off some more tactical examples of things that you can do towards this goal. Savvy readers will note that these are not novel ideas of my own, and in fact a lot of the things on this list are popular core features in modern languages such as Kotlin, Rust, and Clojure. Kotlin, in particular, has done an amazing job of emphasizing these best practices while still being an... - Source: dev.to / 12 months ago
  • Let's write a simple microservice in Clojure
    This article will explain how to write a simple service in Clojure. The sweet spot of making applications in Clojure is that you can expressively use an entire rich Java ecosystem. Less code, less boilerplate: it is possible to achieve more with less. In this example, I use most of the libraries from the Java world; everything else is a thin Clojure wrapper around Java libraries. - Source: dev.to / about 1 year ago
  • A new F# compiler feature: graph-based type-checking
    I have a tangential question that is related to this cool new feature. Warning: the question I ask comes from a part of my brain that is currently melted due to heavy thinking. Context: I write a fair amount of Clojure, and in Lisps the code itself is a tree. Just like this F# parallel graph type-checker. In Lisps, one would use Macros to perform compile-time computation to accomplish something like this, I think.... - Source: Hacker News / over 1 year 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 Clojure and Scikit-learn, you can also consider the following products

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

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

Python - Python is a clear and powerful object-oriented programming language, comparable to Perl, Ruby, Scheme, or Java.

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

Rust - A safe, concurrent, practical language

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

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