Software Alternatives, Accelerators & Startups
Register | Login

statsmodels VS Scikit-learn

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

ASocks
Clear, Fast & Unlimited. Residential & Mobile Proxies For Best Price. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

statsmodels logo statsmodels

Statsmodels: statistical modeling and econometrics in Python - statsmodels/statsmodels

Scikit-learn logo Scikit-learn

scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
  • statsmodels Landing page
    Landing page //
    2023-08-18
  • Scikit-learn Landing page
    Landing page //
    2022-05-06

statsmodels features and specs

No features have been listed yet.

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 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.

statsmodels videos

+ Add

Linear Regressions with StatsModels

More videos:

  • Review - Code review - Z Test using statsmodels
  • Review - Code Review: Analyse Training VAR statsmodels with a real world dataset

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 statsmodels and Scikit-learn)

statsmodels

Scikit-learn

Development Tools
100 100%
Development Tools
0% 0
Data Science And Machine Learning
2 2%
Data Science And Machine Learning
98% 98
Data Science Tools
0 0%
Data Science Tools
100% 100
Application Builder
100 100%
Application Builder
0% 0

User comments

Share your experience with using statsmodels and Scikit-learn. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare statsmodels and Scikit-learn

statsmodels Reviews

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

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 should be more popular than statsmodels. It has been mentiond 35 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.

statsmodels mentions (4)

  • [P] statsmodels.tsa.holtwinters.ExponentialSmoothing results in NaN forecasts and parameters when fitting on entire dataset using known parameters from training model.
    I reckon you're more likely to get a good response on their Github page than here. Unless a dev happens to see this post. Source: almost 3 years ago
  • How do you usually build your models?
    Since you are using python, pandas, scikit-learn, scipy, and statsmodels are what you are looking for. Source: about 3 years ago
  • Can we solve serverless cold starts?
    In case you're really worried about cold start latency and your application load shows high variance in the number of concurrent requests, you might want to get a bit fancier. You could use time-series forecasting to anticipate how many containers should be warmed at each point in time. StatsModels is an open-source project that offers the most common algorithms for working with time-series. Here's a good... - Source: dev.to / about 4 years ago
  • Advice required to choose appropriate software for an assignment
    Can't you get a student discount for Stata? R would definitely be able to handle everything. For Python, have a look through the statsmodel package https://github.com/statsmodels/statsmodels. Source: over 4 years ago

Scikit-learn mentions (35)

  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…. - Source: dev.to / 13 days ago
  • What is the Most Effective AI Tool for App Development Today?
    For apps demanding robust machine learning capabilities, frameworks like TensorFlow provide the scalability and flexibility needed to handle large-scale data and models. These tools are essential for developers building features like recommendation engines or predictive analytics. - Source: dev.to / about 2 months ago
  • Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
    Machine learning (ML) teaches computers to learn from data, like predicting user clicks. Start with simple models like regression (predicting numbers) and clustering (grouping data). Deep learning uses neural networks for complex tasks, like image recognition in a Vue.js gallery. Tools like Scikit-learn and PyTorch make it easier. - Source: dev.to / about 2 months ago
  • Predicting Tomorrow's Tremors: A Machine Learning Approach to Earthquake Nowcasting in California
    Scikit-learn Documentation: https://scikit-learn.org/. - Source: dev.to / 3 months ago
  • 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 / 8 months ago
View more

What are some alternatives?

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

Flutter - Build beautiful native apps in record time 🚀

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

python wiki - Component Libraries

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

Ionic - Ionic is a cross-platform mobile development stack for building performant apps on all platforms with open web technologies.

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

Loading...