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Scikit-learn VS Riskified

Compare Scikit-learn VS Riskified 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.

Riskified logo Riskified

eCommerce fraud prevention solution and chargeback protection guarantee for online merchants. Find out how we can help your company boost revenue from online sales using our machine-learning powered eCommerce fraud protection software.
  • Scikit-learn Landing page
    Landing page //
    2022-05-06
  • Riskified Landing page
    Landing page //
    2023-10-20

Scikit-learn

Website
scikit-learn.org
Pricing URL
-
$ Details
Release Date
-
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Riskified

Website
riskified.com
Pricing URL
Official Riskified Pricing
$ Details
-
Release Date
2012 January
Startup details
Country
United States
State
New York
City
New York
Founder(s)
Assaf Feldman
Employees
500 - 999
Edit details

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.

Riskified features and specs

  • Chargeback Guarantee
    Riskified offers a chargeback guarantee on approved transactions, meaning if a fraudulent transaction is approved by their system, Riskified will cover the cost of the chargeback, providing a financial safety net for merchants.
  • Increased Approval Rates
    Merchants often see increased approval rates because Riskified's advanced algorithms and machine learning models are tailored to accurately identify genuine customers, allowing more legitimate transactions to be approved.
  • Global Solution
    Riskified supports a wide range of payment methods and currencies, making it suitable for merchants with a global presence and varying customer demographics.
  • Seamless Integration
    The platform offers seamless integration with major e-commerce platforms and payment gateways, reducing the time and effort required for merchants to set up and begin protecting transactions.
  • Advanced Analytics
    Riskified provides merchants with detailed analytics and reporting tools, helping them understand transaction patterns, assess risk, and optimize their operations.

Possible disadvantages of Riskified

  • Cost
    The service can be relatively expensive for smaller businesses, especially those with thin margins, as the pricing model typically involves a fee per transaction or a percentage of the transaction value.
  • Complexity
    For businesses without a dedicated team for fraud prevention, understanding and leveraging all the features and data that Riskified provides can be complex and time-consuming.
  • Dependence on External Provider
    Relying on Riskified for fraud prevention places a critical aspect of the business's operations in the hands of an external provider. Any downtime or service issues with Riskified could directly impact transaction processing.
  • False Positives
    While Riskified aims to minimize false positives, there is always a risk that legitimate transactions may be wrongly declined, which can lead to customer dissatisfaction and potential loss of sales.
  • Customization Limits
    Some merchants may find that the level of customization available in Riskified's fraud prevention algorithms and workflows does not fully meet their unique business needs or preferences.

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 Riskified

Overall verdict

  • Riskified is generally considered a good solution for businesses looking to enhance their fraud detection and prevention capabilities. It is especially recommended for online retailers who wish to strike a balance between reducing fraud and maintaining a seamless customer experience.

Why this product is good

  • Riskified is a well-regarded eCommerce fraud prevention platform that leverages machine learning and big data to identify fraudulent transactions and boost conversion rates. It offers comprehensive solutions for chargeback protection, payment optimization, and account security. Many businesses appreciate its ease of integration, detailed analytics, and the ability to increase approval rates while minimizing fraud-related losses.

Recommended for

  • E-commerce companies
  • Online marketplaces
  • Retail businesses with significant online presence
  • Merchants dealing with high volumes of transactions
  • Businesses seeking advanced analytics for fraud insights

Scikit-learn videos

+ Add

Learning Scikit-Learn (AI Adventures)

More videos:

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

Riskified videos

+ Add

Riskified Educational Webinar: Automating The Fraud Review Process (Summer Boot Camp - 2nd Webinar)

More videos:

  • Review - Riskified Educational Webinar: Optimal Manual Review (Summer Boot Camp - 3rd Webinar)
  • Review - Riskified : Nanoleaf case study

Category Popularity

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

Scikit-learn

Riskified

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
eCommerce
0 0%
eCommerce
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Fraud Prevention
0 0%
Fraud Prevention
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 Riskified

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

Riskified Reviews

We have no reviews of Riskified yet.
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Social recommendations and mentions

Based on our record, Scikit-learn seems to be more popular. 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 / 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

Riskified mentions (0)

We have not tracked any mentions of Riskified yet. Tracking of Riskified recommendations started around Mar 2021.

What are some alternatives?

When comparing Scikit-learn and Riskified, 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.

Signifyd - Signifyd is a SaaS-based, enterprise-grade fraud technology solution for e-commerce stores.

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

Kount - eCommerce fraud detection & prevention

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

Sift - Digital Trust & Safety enables your business to grow, innovate, introduce new products, features, and business models โ€“ without increased risk.

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