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Scikit-learn VS Distill.io

Compare Scikit-learn VS Distill.io and see what are their differences

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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.

Distill.io logo Distill.io

Distill.io is one of the advanced page monitoring tools used by professionals to monitor dynamic pages, feeds, and iframes.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Distill.io Landing page
    Landing page //
    2023-07-27

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.

Distill.io features and specs

  • Real-time Monitoring
    Distill.io provides real-time monitoring capabilities, allowing users to track changes on websites as they happen. This is particularly useful for keeping up-to-date with time-sensitive information.
  • Customization
    Users can customize the tracking frequency and specific elements to monitor on a webpage, giving them control over what information they want to track and how often they receive updates.
  • User-friendly Interface
    The platform offers a straightforward and intuitive interface, making it accessible for users without extensive technical skills to set up website monitoring.
  • Multi-platform Support
    Distill.io supports various platforms including web browsers, mobile devices, and desktops, providing users with flexibility in accessing their monitoring data.

Possible disadvantages of Distill.io

  • Limited Free Plan
    The free version of Distill.io has significant restrictions on the number of monitors and frequency of checks, which may not be sufficient for users with extensive monitoring needs.
  • Potentially High Costs
    For users requiring more frequent monitoring or additional features, the cost can escalate, making it less affordable for personal or small business use.
  • Complex Setup for Advanced Users
    While the basic setup is user-friendly, configuring more advanced monitoring options can be complex and challenging for those who are not technically inclined.
  • Browser Dependency
    Distill.io is heavily reliant on web browsers, which could lead to issues if there are changes or updates to browser security settings or compatibility.

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.

Distill.io videos

+ Add

How To Monitor Website Changes? - Distill.io Chrome Extension

More videos:

  • Review - Data Alerts with Distill.io

Category Popularity

0-100% (relative to Scikit-learn and Distill.io)

Scikit-learn

Distill.io

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Monitoring Tools
0 0%
Monitoring Tools
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Website Monitoring
0 0%
Website Monitoring
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 Distill.io

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

Distill.io Reviews

We have no reviews of Distill.io yet.
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Social recommendations and mentions

Based on our record, Distill.io should be more popular than Scikit-learn. It has been mentiond 82 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 (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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Distill.io mentions (82)

  • Tips for people waiting for the Refurbished Steam Deck restock
    First of all, you probably don't want to manually refresh the Steam page to check if the SD is back in stock, so I recommend installing a website monitoring extension. There are a few out there but I can vouch for distill.io because it has a free tier with unlimited local checks. Set the intervals as low as you want, but keep in mind that the last restock lasted about 45 minutes - 1 hour. I personally set mine to... Source: over 1 year ago
  • Website change trackers
    Recently found out about website change trackers and had to share, like distill.io and fetchnotifs (not affiliated btw). I use them like everyday for amazon items and general stuff. Source: almost 2 years ago
  • Canyon Endurace CF out of stock
    You can also try notify-me.rs . It's super simple tracker, and has more checks than distill.io , we have bunch of people from this subreddit using it already! Source: almost 2 years ago
  • NEW SERIES OF JEFF DRAWINGS ON SALE
    Were these even advertised anywhere online? Insane how fast the 6x8s went. Glad I've had a distill.io alert set there for like 2 years now haha. Source: almost 2 years ago
  • What is the best free tool or code to monitor visual changes to a website in real time? (e.g. Distill.io)
    Distill.io offers every 5 seconds but it seems I need an upgraded account to be able to select this interval using the Device "Cloud-Distill's Severs (this device)". So it seems I'd need to always be on my computer in order for it to run. So I selected "Any Local Device (beta)" for device. Description for "Device" = Select device that this monitor runs on. Other devices will appear in the list once all devices... Source: almost 2 years ago
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What are some alternatives?

When comparing Scikit-learn and Distill.io, 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.

Visualping - Visualping is the easiest to use website checker, webpage change monitoring, website change detector and website change alert software of the web. Read more about Visualping.

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

Monity.ai - Monitor website changes in real time with Monity.ai – Stay informed with AI-powered alerts and never miss anything happening on the internet.

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

Browse AI - Automate any workflow on any website with no code. Used for monitoring, testing, automation, and data aggregation.Sign up now for free and receive 2x jobs per month – forever!

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