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Scikit-learn VS Chart.js

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

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  3. » Reviews
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Scikit-learn logo Scikit-learn

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

Chart.js logo Chart.js

Easy, object oriented client side graphs for designers and developers.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Chart.js Landing page
    Landing page //
    2023-03-13

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.

Chart.js features and specs

  • Open Source
    Chart.js is open source and free to use, which makes it accessible for both personal and commercial projects without any licensing costs.
  • Ease of Use
    Chart.js is known for its simple and easy-to-use API. Developers can quickly create charts by just including the library and writing minimal JavaScript.
  • Lightweight
    The library is relatively lightweight compared to other charting libraries, which helps in maintaining the performance of web applications.
  • Responsive Design
    Charts created with Chart.js are responsive by default, ensuring that they look good on all devices, including desktops, tablets, and mobile phones.
  • Variety of Chart Types
    Chart.js supports a variety of chart types including line, bar, radar, pie, doughnut, and polar area charts, providing flexibility for different data visualization needs.
  • Customization
    Developers can customize the appearance of charts extensively through Chart.js options such as colors, labels, and tooltips.
  • Active Community
    Chart.js has an active community and a strong support base, which means that developers can easily find help, tutorials, and plugins to enhance functionality.

Possible disadvantages of Chart.js

  • Limited Advanced Features
    While Chart.js is good for basic and intermediate charting needs, it may lack some advanced features and customizations offered by more complex charting libraries like D3.js.
  • Performance Issues with Large Datasets
    Chart.js can struggle with performance when dealing with very large datasets or complex visualizations, which can result in slower rendering times.
  • Learning Curve for Customization
    Although the basic usage is straightforward, achieving deeper customizations can involve a steeper learning curve as it requires understanding the underlying JavaScript and options.
  • Limited Interactivity
    Interactivity options with Chart.js are somewhat limited compared to other libraries that offer more advanced interactive features.
  • Dependency on Canvas
    Charts are rendered using the HTML5 canvas element, which may not be as flexible as SVG-based rendering used by some other libraries.

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Chart.js videos

+ Add

1.3: Graphing with Chart.js - Working With Data & APIs in JavaScript

More videos:

  • Tutorial - How to Build Ionic 4 Apps with Chart.js

Category Popularity

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

Scikit-learn

Chart.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
Data Visualization
0 0%
Data Visualization
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 Chart.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

Chart.js Reviews

6 JavaScript Charting Libraries for Powerful Data Visualizations in 2023
Of the free libraries on this list, ECharts has the widest range of chart types available, second only to D3. Unlike D3, ECharts also ranks highly on the user-friendliness scale, although some users find ApexCharts and Chart.js even easier to use. You can check out some examples of basic charts on ECharts.
Source: embeddable.com
5 top picks for JavaScript chart libraries
Chart.js is a chart library that is available as a client-side JavaScript package. There are also derivatives for other frontend frameworks, like React, Vue, and Angular. It displays the chart on an HTML canvas element.
Source: blog.logrocket.com
Top 10 JavaScript Charting Libraries for Every Data Visualization Need
Chart.js is a simple yet quite flexible JavaScript library for data viz, popular among web designers and developers. It’s a great basic solution for those who don’t need lots of chart types and customization features but want their charts to look neat, clear and informative at a glance.
Source: hackernoon.com
A Complete Overview of the Best Data Visualization Tools
Chart.js uses HTML5 Canvas for output, so it renders charts well across all modern browsers. Charts created are also responsive, so it’s great for creating visualizations that are mobile-friendly.
Source: www.toptal.com
The Best Data Visualization Tools - Top 30 BI Software
Chart.js is better for smaller chart projects. It’s open source and small in size, supporting six different types of charts: bar, line, pie, radar, doughnut, and polar. You can also add or remove any of these 6 types to reduce your footprint. Chart.js uses HTML5 Canvas and ships with polyfills for IE6/7 support. Chart.js offers the ability to create simple charts quickly.
Source: improvado.io

Social recommendations and mentions

Based on our record, Scikit-learn seems to be a lot more popular than Chart.js. While we know about 31 links to Scikit-learn, we've tracked only 1 mention of Chart.js. 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 (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
View more

Chart.js mentions (1)

  • Chart library for Svelte?
    Https://chartjs.org works well, but you have to call the update function yourself if you want to do some reactive updates. Source: almost 4 years ago

What are some alternatives?

When comparing Scikit-learn and Chart.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.

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

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

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

Google Charts - Interactive charts for browsers and mobile devices.

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