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Scikit-learn VS Delighted

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

Seranova
Seranova AI helps home service businesses automate review outreach, stay on top of customer conversations, and grow reputation without extra headcount. featured
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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.

Delighted logo Delighted

The fastest and easiest way to gather actionable feedback from your customers
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Delighted Landing page
    Landing page //
    2023-10-19

Deliver customer feedback and employee experience surveys across various channels. Control when and where surveys are delivered for point-in-time feedback at key points in the customer journey and employee lifecycle.

No code required.

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.

Delighted features and specs

  • Survey Distribution via Email, Web, Link, SDK and Kiosk
  • Prebuilt Reports and Trend Reports
    Yes
  • AI Suggested Filters
    Yes
  • Customize Branding
    Yes
  • Survey Types: NPS, CSAT, CES, 5-Star, Thumbs, Smiley, eNPS, PMF
  • Send Survey from Your Domain
    Yes
  • 30+ Languages
  • Autopilot to Schedule Delivery
    Yes
  • Survey Templates
    Yes
  • Additional Questions
    Yes
  • Testimonials

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.

Analysis of Delighted

Overall verdict

  • Yes, Delighted is generally considered a good tool for collecting customer feedback and measuring customer satisfaction.

Why this product is good

  • Delighted is known for its simplicity and effectiveness in gathering Net Promoter Score (NPS) data. It offers a user-friendly interface, easy integration with various platforms, and real-time feedback collection. Delighted helps businesses understand customer sentiment and improve their products or services based on the feedback received.

Recommended for

  • Businesses looking to implement NPS surveys quickly and efficiently
  • Companies seeking real-time feedback from customers
  • Organizations that want to integrate feedback tools with existing platforms like Slack, Salesforce, and Shopify
  • Small to medium-sized businesses aiming for a cost-effective customer feedback solution

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Delighted videos

+ Add

Delighted used by Bonobos

More videos:

  • Demo - NPS Setup in 15 Seconds

Category Popularity

0-100% (relative to Scikit-learn and Delighted)

Scikit-learn

Delighted

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Customer Feedback
0 0%
Customer Feedback
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Surveys
0 0%
Surveys
100% 100

User comments

Share your experience with using Scikit-learn and Delighted. For example, how are they different and which one is better?
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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 Delighted

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

Delighted Reviews

12 Best SurveySparrow Alternatives With Pricing and Features
A menโ€™s clothing and accessories brand, Bonobos, wanted to know how customers feel about the new shipping process. The brand used a Delighted NPS survey and found that the customers were not happy with the changes.
Source: qualaroo.com

Social recommendations and mentions

Based on our record, Scikit-learn seems to be a lot more popular than Delighted. While we know about 40 links to Scikit-learn, we've tracked only 2 mentions of Delighted. 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 / about 2 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 / 2 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 / 2 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 / 3 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 / 5 months ago
View more

Delighted mentions (2)

  • MSP Feedback Survey / Net Promoter - Third Party Executed
    We've used https://delighted.com and been pretty happy with it. It's sent to customers during "key intersects" (onboarding, after projects, etc.) and after events. The results stream into Teams and we also track/analyze them to improve service. Source: over 4 years ago
  • Creating a simpler NPS, CSAT, CES service like Delighted?
    After seeing a business idea newsletter mention a SaaS that help monitor you NPS score, I decided to look a little more into it. There are a LOT of solutions out there, but they're also wildly expensive since I assume they're targeting larger organizations. One service I found, https://delighted.com, provides simple forms and widgets to collect NPS, CSAT, CES and other and displays the results in a simple... Source: about 5 years ago

What are some alternatives?

When comparing Scikit-learn and Delighted, 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.

Survicate - Collect feedback on your website and find out more about your visitors.

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

AskNicely - Collect customer experience feedback on a daily basis and empower your team to take immediate action to drive retention, upgrades, reviews and referrals.

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

Wootric - Wootric is software that allows apps and websites to take customer satisfaction surveys so that you can properly gauge the popularity and success of your app through the eyes of the people using it. Read more about Wootric.

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