Software Alternatives & Startups
Register | Login

Scikit-learn VS NumeRe

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

Scikit-learn

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

Scikit-learn Landing page
Rating
0 reviews
Pricing
Open source
Alternatives Is it good?
NumeRe

Framework for numerical computations, data analysis and visualisation.

NumeRe Session view
Rating
0 reviews
Pricing
Open source Free
Alternatives Is it good?
Grapple
Do-It-Yourself Data Analytics & Business Intelligence, Powered by AI sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, Scikit-learn seems to be more popular. It has been mentioned 40 times since March 2021.

social mentions
40 vs 0
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
240+ vs 84

Base details

Website, pricing, platforms and company facts side by side.

Scikit-learn
NumeRe
Website scikit-learn.org numere.org
Pricing
Open source
Open source Free
Platforms —
Windows
Company — Startup from Germany · 1 - 9 employees · 2025
Listed in
Data Science And Machine Learning Data Science Tools Python Tools Software Libraries +1
Analytics Data Visualization Numerical Computation Technical Computing +1

About Scikit-learn and NumeRe

In their own words, as submitted to SaaSHub.

Scikit-learn
NumeRe

No description of Scikit-learn yet.

Solving and visualizing. Table based. Statistics and numerics. Optimized for science. Free of charge. GNU GPL v3. NumeRe: Framework for Numerical Computations is an application for Microsoft Windows® that can do more than the usual spreadsheets. It provides you with nonlinear fits of arbitrary...

Read more about NumeRe

Features and specs

What each product offers, as listed by its team.

Scikit-learn 5 features
NumeRe 6 features
  • 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

  • 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.
  • Scripting support
  • Fitting
  • File Versioning
  • Syntax Highlighting
  • Autocompletion
  • Import CSV data

Analysis

An editorial look at what each product does well and who it suits.

Scikit-learn
NumeRe

Overall verdict

  • Yes, Scikit-learn is generally regarded as a good library for machine learning, especially for beginners and intermediate users who need reliable tools with efficient implementation of numerous algorithms.

Why this product is good

  • Scikit-learn is considered a good machine learning library because it provides a wide range of state-of-the-art algorithms for supervised and unsupervised learning. It is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is well-documented, easy to use, and has a consistent API that simplifies the integration of different algorithms. Furthermore, there's a strong community and continuous development, which means it is well-maintained and updated regularly with new features and improvements.

Recommended for

  • Beginners learning machine learning concepts and application.
  • Data scientists and engineers looking for a robust and efficient toolkit to build and deploy machine learning models.
  • Researchers who need an easy-to-use library that facilitates the experimentation of various algorithms.
  • Developers who require a seamless, Python-based machine learning library that integrates well with other data analysis tools and environments.

No analysis of NumeRe yet.

Videos

Walkthroughs and reviews on video.

Scikit-learn 2 videos + Add
NumeRe 0 videos + Add

Learning Scikit-Learn (AI Adventures)

More videos

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

No NumeRe videos yet. You could help us improve this page by suggesting one.

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Scikit-learn
NumeRe
100% 100%
Data Science And Machine Learning
0% 0%
0% 0%
Technical Computing
100% 100%
100% 100%
Data Science Tools
0% 0%
0% 0%
Numerical Computation
100% 100%

Questions & Answers

As answered by people managing Scikit-learn and NumeRe.

What makes your product unique?

NumeRe's answer:

  • An advanced built-in editor
  • Syntax following "Different things should look different" approach
  • Lightweight installation
  • No hard dependencies (LaTeX is optional)
  • Built-in version control management
  • and many more ...

Which are the primary technologies used for building your product?

NumeRe's answer:

NumeRe is built using mainly C++ together with some minor code snippets from C. A large variety of additional libraries is used, but most code has been written from scratch.

How would you describe the primary audience of your product?

NumeRe's answer:

  • Data analysts and persons interested in this field
  • People familiar with spreadsheets like Excel but wanting more elaborate functionalities
  • Students, teachers, scientific edcutators

What's the story behind your product?

NumeRe's answer:

You can read about it here: https://en.numere.org/about/further-information

Why should a person choose your product over its competitors?

NumeRe's answer:

If you're coming from Excel (or similar), you might want to read those two articles: https://en.numere.org/home/blog/can-numere-excel and https://en.numere.org/home/blog/when-numere-is-the-better-spreadsheet

Besides that: feel free to scan through our blog, where we post regularly about NumeRe's features: https://en.numere.org/home/blog

User comments

Share your experience with using Scikit-learn and NumeRe. For example, how are they different and which one is better?

Log in or Post with

Reviews and articles

External articles and on-site reviews we used to compare the two products.

Scikit-learn no reviews yet
NumeRe no reviews yet

We have no reviews of NumeRe yet. Be the first one to post

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Scikit-learn 40 mentions
NumeRe 0 mentions
  • Detecting Ingress Tool Transfer (T1105) with Python
    Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process,... - Source: dev.to / 4 months ago
  • Best AI Cybersecurity Training for Security Teams: How to Pick
    Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab.... - Source: dev.to / 4 months ago
  • Where to Get Hands-On AI Training for Cybersecurity Professionals
    Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable... - Source: dev.to / 4 months ago

View more

Tracking NumeRe since Feb 2023.

Alternatives to Scikit-learn and NumeRe

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

Loading...