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Scikit-learn VS Plotly.js

Compare Scikit-learn VS Plotly.js and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Scikit-learn logo Scikit-learn

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

Plotly.js logo Plotly.js

Open-source JavaScript charting library behind Plotly and Dash - plotly/plotly.js
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Plotly.js Landing page
    Landing page //
    2023-09-26

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.

Plotly.js features and specs

  • Interactive Visualizations
    Plotly.js provides highly interactive charting capabilities, allowing users to hover, zoom, and pan easily within charts, enhancing the user experience.
  • Wide Range of Chart Types
    The library supports a comprehensive variety of chart types, from simple line and bar charts to more complex types like histograms, scatter plots, and even 3D and geographic plots.
  • Cross-Platform Compatibility
    Plotly.js works seamlessly across different platforms and browsers, ensuring consistent chart rendering and functionality whether used on desktops or mobile devices.
  • Customizable
    Users have a high degree of control over the appearance and behavior of plots, with numerous options to customize colors, legends, tooltips, and more.
  • Integration with Dash
    Plotly.js integrates well with the Dash framework, enabling the creation of interactive web applications that are highly visual and data-driven.

Possible disadvantages of Plotly.js

  • Performance Concerns
    Rendering very large datasets can be slow, potentially impacting the performance of the application, particularly in resource-constrained environments.
  • Complexity
    For users new to the library, the learning curve can be steep due to the extensive options and configurations available.
  • Size of Library
    Plotly.js has a relatively large file size, which can be a concern for web applications where minimizing load times and data transfer is critical.
  • Limited Free Features
    Certain advanced features and functionalities may require a paid subscription to Plotly's services, which may not be ideal for all users or projects.
  • Dependency Management
    Managing dependencies and ensuring compatibility with other JavaScript libraries can sometimes pose challenges, especially in complex applications.

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.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Plotly.js videos

+ Add

[Beginner] Simple Bar Chart | React Plotly.js

More videos:

  • Tutorial - Create Real-time Chart with Javascript | Plotly.js Tutorial

Category Popularity

0-100% (relative to Scikit-learn and Plotly.js)

Scikit-learn

Plotly.js

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Charting Libraries
0 0%
Charting Libraries
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Libraries
0 0%
Libraries
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 Scikit-learn and Plotly.js

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

Plotly.js Reviews

15 JavaScript Libraries for Creating Beautiful Charts
Plotly.js is the first scientific JavaScript charting library for the web. It has been open-source since 2015, meaning anyone can use it for free. Plotly.js supports 20 chart types, including SVG maps, 3D charts, and statistical graphs. Itโ€™s built on top of D3.js and stack.gl.
Source: www.sitepoint.com
Top 10 JavaScript Charting Libraries for Every Data Visualization Need
Plotly.js is a high-level JavaScript library, free and open-source. It is built on D3.js and WebGL, so can be used to create many different chart types including 3D charts to statistical graphs.
Source: hackernoon.com

Social recommendations and mentions

Based on our record, Scikit-learn should be more popular than Plotly.js. It has been mentiond 40 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.

Scikit-learn mentions (40)

  • 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, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / 3 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. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 3 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 lab. No setup tax. - Source: dev.to / 3 months ago
  • How Anomaly Detection Actually Works in Security Operations
    Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 4 months ago
  • Building a Personalized Meal Recommendation System
    In practice, youโ€™ll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 6 months ago
View more

Plotly.js mentions (4)

  • Weekly JavaScript Roundup: Friday Links 17, February 07, 2025
    Plotly.js - Open-source JavaScript charting library behind Plotly and Dash. - Source: dev.to / over 1 year ago
  • Ask HN: What packages can be used to create interactive mathematics simulations?
    Well, MathML[1] support is (nearly) everywhere now, and as the docs say: MathML Core is a subset with increased implementation details based on rules from LaTeX and the Open Font Format. It is tailored for browsers and designed specifically to work well with other web standards including HTML, CSS, DOM, JavaScript. I don't have a lot of experience working with this stuff (yet) but if you can script your... - Source: Hacker News / about 3 years ago
  • What's new in Matplotlib 3.7.0 (Feb 13, 2023)
    Plotly offers multiple options (python, R, javascript). The weby stuff is done with plotly.js and uses d3.js underneath - https://github.com/plotly/plotly.js. - Source: Hacker News / over 3 years ago
  • How to decide between Dash versus Flask + React + Plotly.js?
    So you didn't use Django DRF as the backend? I'm just curious how Dash communicated with Django - did it communicate via plain HTTP calls? I guess you ran non-React Plotly.js (https://github.com/plotly/plotly.js)? Source: about 5 years ago

What are some alternatives?

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

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

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

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

Highcharts - A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application

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

Chart.js - Easy, object oriented client side graphs for designers and developers.

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