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Qalculate! VS Scikit-learn

Compare Qalculate! VS Scikit-learn and see what are their differences

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Qalculate! logo Qalculate!

Qalculate! is a multiplatform multi-purpose desktop calculator.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • Qalculate! Landing page
    Landing page //
    2023-10-11
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

Qalculate! features and specs

  • Versatility
    Qalculate! supports a wide range of calculations, including basic arithmetic, algebra, calculus, and complex mathematical functions, making it suitable for various users from students to professionals.
  • Extensive Unit Conversions
    It provides extensive support for unit conversions across different measurement systems, which is very useful for scientific and engineering computations.
  • Currency Conversion
    The tool includes real-time currency conversion capabilities, allowing users to perform financial calculations with current exchange rates.
  • Customizability
    Users can define their own functions and variables, offering a high degree of customization to cater to specific needs.
  • User-Friendly Interface
    Qalculate! features an intuitive and user-friendly interface, making it accessible even to those who are not highly technically proficient.
  • Cross-Platform
    It is available on multiple operating systems, including Windows, macOS, and Linux, ensuring accessibility for a wide user base.
  • Free and Open Source
    Being open-source and free to use, it offers a cost-effective solution compared to commercial software without compromising on features.

Possible disadvantages of Qalculate!

  • Learning Curve
    Despite its user-friendly interface, the vast array of features and functionalities may present a steep learning curve for new users.
  • Documentation
    While there is documentation available, it may not be as comprehensive or as user-friendly as some users might require, making it challenging to fully utilize all features.
  • Performance
    For very large or complex calculations, the performance might not be as robust or fast as some specialized or commercial tools.
  • GUI Limitations
    The graphical user interface (GUI) might have limitations in presenting very complex calculations or notations as compared to some professional-grade mathematical software.
  • Lack of Community Support
    Being a niche tool, it may not have as large of a community for support and resources as more popular commercial alternatives.

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.

Analysis of Qalculate!

Overall verdict

  • Yes, Qalculate! is considered a good tool by many users, especially those who require more than a basic calculator. Its powerful features and ease of use make it a valuable tool for anyone needing a reliable and multifunctional calculator.

Why this product is good

  • Qalculate! is highly regarded for its versatility as a desktop calculator program. It offers a wide range of mathematical functions, unit conversions, and a user-friendly interface. It's particularly noted for its ability to handle complex calculations, customizable features, and comprehensive library of functions, making it suitable for both casual and advanced users. Additionally, it's open-source, which allows for community contributions and improvements.

Recommended for

    Qalculate! is recommended for students, engineers, scientists, and anyone who requires a robust computing tool for everyday calculations, unit conversions, and advanced mathematical functions. It's also ideal for users who appreciate open-source software and those looking for a customizable and comprehensive calculator.

Analysis of Scikit-learn

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.

Qalculate! videos

+ Add

DSP Raspberry Pi 4 Qalculate! Install

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 Qalculate! and Scikit-learn)

Qalculate!

Scikit-learn

Calculators
100 100%
Calculators
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Advanced Calculator
100 100%
Advanced Calculator
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 Qalculate! and Scikit-learn

Qalculate! 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

Qalculate! might be a bit more popular than Scikit-learn. We know about 34 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.

Qalculate! mentions (34)

  • Show HN: Unsure Calculator – back-of-a-napkin probabilistic calculator
    Https://qalculate.github.io can do this also for as long as I've used it (only a couple years to be fair). I've got it on my phone, my laptop, even my server with the qalc command. Super convenient, supports everything from unit conversion to uncertainty tracking The histogram is neat, I don't think qalc has that. On the other hand, it took 8 seconds to calculate the default (exceedingly trivial) example. Is that... - Source: Hacker News / about 1 month ago
  • Frink
    Interesting project. I use command line Qalculate [1] for this (has a very similar feature set to Frink AFAICT) and Pint [2] for scripting. I feel like unit-aware calculators are hugely underused by physical engineers, it's the same idea and benefit as type safety but they're virtually unheard of, everyone just uses excel. Having guaranteed dimensional correctness is so great for the early design stage, it makes... - Source: Hacker News / 2 months ago
  • "A calculator app? Anyone could make that."
    I use qalculate, it behaves well enough for my needs. https://qalculate.github.io/. - Source: Hacker News / 3 months ago
  • Students, what features would you like to see on Windows 12?
    1) a scientific calculator with history and variables with a UI similar to https://sourceforge.net/projects/alt1-calculator/ that also can do units like https://qalculate.github.io/ 2) a tiny text chat direct message program that is similarly as easily accessible at Atl1 3) a minimalist dock of as many instances you would like similar to https://punklabs.com/rocketdock, and like where WIN opens the start menu, WIN... Source: over 1 year ago
  • Paint on Windows is getting layers and transparency support
    Qalculate is my go-to for cross platform calculator that is useful and is not limited to the most basic +-*/ operations. https://qalculate.github.io/. - 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 / 4 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 / 12 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 Qalculate! and Scikit-learn, you can also consider the following products

SpeedCrunch - SpeedCrunch. SpeedCrunch is a high-precision scientific calculator featuring a fast, keyboard-driven user interface. It is free and open-source software, licensed under the GPL. Download Documentation Donate .

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

Numi App - Numi is a beautiful text calculator for Mac.

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

Event Viewer - Get help, support, and tutorials for Windows products—Windows 10, Windows 8.1, Windows 7, and Windows 10 Mobile.

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

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