Predictive Analytics Times
Predictive Analytics Times
EXCLUSIVE HIGHLIGHTS
Data Reliability and Validity, Redux: Do Your CIO and Data Curators Really Understand the Concepts?
 Here are two recent entries on...
On Variable Importance in Logistic Regression
 The model looks good. It’s parsimonious,...
Data-Driven Decisions for Law Enforcement in Toronto
 For today’s leading deep learning methods...
AI, Machine Learning, and the Basics of Predictive Analytics for Process Management
 APQC Chair Carla O’Dell interviews Predictive...
SHARE THIS:

4 years ago
Haystacks and Needles: Anomaly Detection

 Anomalies vs Outliers Anomaly detection, or finding needles in a haystack, is an important tool in data exploration and unsupervised analytic modeling. Anomaly detection also creates a path to supervised modeling by singling out key examples that an analyst can begin to classify as needles or hay. Those labeled examples are essential for supervised learning, which is much more powerful than unsupervised learning methods like clustering. Though anomaly and outlier are often used interchangeably we’d like to emphasize distinct definitions. As Ravi Parikh describes well in a blog post[1], “An outlier is a legitimate data point that’s far away from

To view this content
Login OR subscribe for free

Already receive the Predictive Analytics Times emails?
As of January 2014, the Predictive Analytics Times now requires legacy email subscribers to upgrade their subscription - one time only - in order to attain a password-protected login and gain complete access.

Click here to complete this one-time subscription upgrade

  

Existing Users Log In
   
New User Registration
*Required field

Comments are closed.