Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Scalarr offers a cutting-edge solution for mobile ad fraud detection through advanced Machine Learning technology. To combat the most significant threats in mobile advertising, Scalarr employs a dual-layered approach with next-generation algorithms that achieve an impressive accuracy rate of up to 97% in identifying various forms of in-app fraud. Users can experience the benefits of Scalarr by exploring its capabilities to overview, analyze, and thwart mobile app install ad fraud using its unsupervised machine learning features before any harm occurs. The platform utilizes both unsupervised and semi-supervised machine learning techniques to automatically spot and understand fraud patterns across vast datasets. By examining countless clicks, installs, and post-install event variables, Scalarr significantly minimizes both false positives and false negatives in its detection process. With a sophisticated model design prioritizing result accuracy and thoroughness, Scalarr stands out as a robust tool that provides actionable insights at the individual conversion level, ensuring advertisers can make informed decisions regarding their ad campaigns. This comprehensive approach ultimately enhances the overall integrity of mobile advertising strategies.
Description
Scikit-learn offers a user-friendly and effective suite of tools for predictive data analysis, making it an indispensable resource for those in the field. This powerful, open-source machine learning library is built for the Python programming language and aims to simplify the process of data analysis and modeling. Drawing from established scientific libraries like NumPy, SciPy, and Matplotlib, Scikit-learn presents a diverse array of both supervised and unsupervised learning algorithms, positioning itself as a crucial asset for data scientists, machine learning developers, and researchers alike. Its structure is designed to be both consistent and adaptable, allowing users to mix and match different components to meet their unique requirements. This modularity empowers users to create intricate workflows, streamline repetitive processes, and effectively incorporate Scikit-learn into expansive machine learning projects. Furthermore, the library prioritizes interoperability, ensuring seamless compatibility with other Python libraries, which greatly enhances data processing capabilities and overall efficiency. As a result, Scikit-learn stands out as a go-to toolkit for anyone looking to delve into the world of machine learning.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Adjust
Yes
AppsFlyer
Yes
DagsHub
No
Databricks
No
Flower
No
GLM-5.1
No
GLM-5.2
No
GLM-5.3
No
Guild AI
No
Keepsake
No
Integrations
Adjust
No
AppsFlyer
No
DagsHub
Yes
Databricks
Yes
Flower
Yes
GLM-5.1
Yes
GLM-5.2
Yes
GLM-5.3
Yes
Guild AI
Yes
Keepsake
Yes
Pricing Details
No price information available.
Free Trial
Yes
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Scalarr
Founded
2016
Country
United States
Website
scalarr.io
Vendor Details
Company Name
scikit-learn
Country
United States
Website
scikit-learn.org/stable/
Product Features
Click Fraud
Account Alerts
No
Activity Monitoring
No
IP Address Monitoring
No
IP Blocking
No
Keyword Tracking
No
Refund Management
No
Risk Assessment
No
Time on Site Tracking
No
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No