Software Alternatives, Accelerators & Startups

hub VS Scikit-learn

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

hub logo hub

The Hub is a versatile intranet portal and collaboration solution that boosts employee engagement and productivity in a digital workplace.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • hub Landing page
    Landing page //
    2021-09-14
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

hub videos

Speedone Sniper 150T Rachet | Hub Review & Soundcheck

More videos:

  • Review - Nissan Sunny B211 (B210 Facelift) Review (Sinhala) | Auto Hub
  • Review - Fanatec CSW Universal Hub Review

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 hub and Scikit-learn)
Communication
100 100%
0% 0
Data Science And Machine Learning
Project Management
100 100%
0% 0
Data Science Tools
0 0%
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 hub and Scikit-learn

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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...

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than hub. It has been mentiond 29 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.

hub mentions (4)

  • GitHub Discussion about the recent feed changes becomes 3rd most upvoted ever
    Use hub here via CLI and forget the gui https://hub.github.com/. - Source: Hacker News / 10 months ago
  • Pull request Best Practices
    Try automating the PR process as much as possible. Make use of tools like hub CLI for speeding up the pull request process. Code quality tools can help you automate the due diligence for coding standards and conventions, and test automation tools can assist in bug discovery, and identifying security vulnerabilities. - Source: dev.to / about 1 year ago
  • [Media] I made a Rust CLI game that tests how fast you can guess the language of a code block!
    Parse_git_branch() { # Speed up opening up a new terminal tab by not # checking `$HOME` ...which can't be a repo anyway # # For the heck of it, micro-optimize this too: # time (repeat 1000000 { [ "$PWD" = "$HOME" ] } ) == ~4.2s # time (repeat 1000000 { [[ "$PWD" == "$HOME" ]] } ) == ~1.4s [[ "$PWD" == "$HOME" ]] && return # Fastest known way to check the current branch name ... Source: almost 2 years ago
  • I have 20 repositories, is there any way I can create a report showing how many open issues in each?
    You can always query via github api or use the hub client (from their home page https://hub.github.com/). Source: over 2 years ago

Scikit-learn mentions (29)

  • 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 / 20 days 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 / 4 months 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 / about 1 year ago
  • WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
    The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago
  • PSA: You don't need fancy stuff to do good work.
    Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago
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What are some alternatives?

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

LS Intranet - LS Intranet is a corporate intranet that facilitates multiple employees collaborating on projects, and working in synergy.

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

Communifire - Enterprise Social Collaboration Software

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

Powell-365 - Digital workplace solution for businesses of all sizes

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