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OpenGrok VS Scikit-learn

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

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

OpenGrok logo OpenGrok

OpenGrok is a fast and usable source code search and cross reference engine.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • OpenGrok Landing page
    Landing page //
    2021-10-20
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

OpenGrok features and specs

  • Efficient Code Search
    OpenGrok provides powerful full-text code search capabilities, which allow developers to quickly find relevant code fragments, classes, and functions across potentially large codebases.
  • Source Code Navigation
    It facilitates easy navigation through source code, enabling users to explore code structure, variable definitions, and references, enhancing understanding and productivity.
  • Supports Multiple Version Control Systems
    OpenGrok is compatible with various version control systems such as Git, Mercurial, and Subversion, making it versatile and adaptable to different development environments.
  • Web Interface
    The tool provides a user-friendly web interface, allowing remote access to code repositories and making it easier for teams to collaborate and share code insights.
  • Cross-Referencing
    OpenGrok includes cross-referencing capabilities that enable developers to identify and analyze code dependencies and connections, improving code comprehension and maintenance.

Possible disadvantages of OpenGrok

  • Initial Setup Complexity
    Setting up OpenGrok can be challenging, requiring considerable configuration and resources, particularly for large and complex codebases.
  • Resource Intensive
    The tool can be resource-intensive, requiring substantial CPU and memory, especially when indexing large repositories, which may impact performance.
  • Limited Language Support
    OpenGrok may not support all programming languages natively for indexing and searching, potentially limiting its applicability in heterogeneous environments.
  • Maintenance Overhead
    Ensuring that OpenGrok remains efficient and up-to-date can entail ongoing maintenance, including regular updates and re-indexing of repositories.
  • Scalability Challenges
    While OpenGrok is powerful, scaling it for very large enterprise environments or numerous users can present challenges, requiring infrastructure considerations and optimizations.

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.

OpenGrok videos

+ Add

How to setup Opengrok on Linux (In less than 2 minutes)

More videos:

  • Review - Writing and Rewriting Web Apps in nginx.conf — URL shortening, OpenGrok05 by Constantine Murenin

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 OpenGrok and Scikit-learn)

OpenGrok

Scikit-learn

Git
100 100%
Git
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Code Collaboration
100 100%
Code Collaboration
0% 0
Data Science Tools
0 0%
Data Science Tools
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 OpenGrok and Scikit-learn

OpenGrok Reviews

We have no reviews of OpenGrok yet.
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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, 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.

OpenGrok mentions (0)

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

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 OpenGrok and Scikit-learn, you can also consider the following products

Sourcegraph - Sourcegraph is a free, self-hosted code search and intelligence server that helps developers find, review, understand, and debug code. Use it with any Git code host for teams from 1 to 10,000+.

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

Atlassian Fisheye - With FishEye you can search code, visualize and report on activity and find for commits, files, revisions, or teammates across SVN, Git, Mercurial, CVS and Perforce.

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

Krugle - Krugle is the complete enterprise solution for search targeted to the development organization. 

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

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