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SymPy VS Scikit-learn

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

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Contents:
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  3. » Reviews
  4. » Alternatives

SymPy logo SymPy

SymPy is a Python library for symbolic computation.

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • SymPy Landing page
    Landing page //
    2021-12-24
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

SymPy features and specs

  • Symbolic Computation
    SymPy provides robust support for symbolic mathematics, allowing users to perform algebraic manipulations, calculus, equation solving, and more, symbolically rather than numerically, which can be crucial for exact computations.
  • Open Source and Free
    As an open-source library, SymPy is free to use, modify, and distribute, offering transparency and community contributions to enhance its functionality and reliability.
  • Integration with Python
    SymPy is implemented in Python, which makes it easy to integrate into Python-based workflows and take advantage of other powerful libraries within the Python ecosystem.
  • Extensive Documentation
    SymPy has comprehensive documentation and a large number of tutorials and resources available, which aids users in learning and effectively using the library.
  • Cross-Platform
    Being a Python library, SymPy can be used on any platform that supports Python, ensuring wide accessibility regardless of the operating system.
  • Interactive Use
    SymPy can be used interactively in a variety of environments, such as Jupyter notebooks, which makes it excellent for educational purposes and exploratory computing.

Possible disadvantages of SymPy

  • Performance Limitations
    Since SymPy is purely Python, it may suffer from performance issues, particularly with very large symbolic expressions, compared to libraries implemented in lower-level languages.
  • Numerical Limitations
    SymPy is primarily a symbolic computation library and may not be suitable or optimized for numerical computations compared to dedicated numerical libraries like NumPy or SciPy.
  • Complexity with Large Problems
    For highly complex or large-scale mathematical problems, SymPy can become cumbersome and may require significant memory and computation time.
  • Steeper Learning Curve for Complex Tasks
    While basic functionalities are easy to grasp, mastering advanced features of SymPy can be challenging due to the depth and breadth of its capabilities.

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.

SymPy videos

+ Add

Python Sympy Integrals

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 SymPy and Scikit-learn)

SymPy

Scikit-learn

Technical Computing
100 100%
Technical Computing
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Numerical Computation
100 100%
Numerical Computation
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 SymPy and Scikit-learn

SymPy Reviews

4 open source alternatives to MATLAB
SymPy, another BSD-licensed Python library for symbolic mathematics. It can be installed on any computer running Python. It aims to become a full computer algebra system; has an active development community with regular releases; and is used in many other projects (including SageMath, above).
Source: opensource.com
3 Open Source Alternatives to MATLAB
SymPy, another BSD-licensed Python library for symbolic mathematics. It can be installed on any computer running Python 2.7 or above. It aims to become a full computer algebra system; has an active development community with regular releases; and is used in many other projects (including SageMath, above).
Source: www.openhealthnews.com

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.

SymPy mentions (0)

We have not tracked any mentions of SymPy yet. Tracking of SymPy 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 SymPy and Scikit-learn, you can also consider the following products

C++ - Has imperative, object-oriented and generic programming features, while also providing the facilities for low level memory manipulation

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

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

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

D (Programming Language) - D is a language with C-like syntax and static typing.

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

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