Unsupervised learning: Anomaly Detection with PyCaret Workflow
The Anomaly Detection with PyCaret Workflow will help you discover and visualize anomalies in large web-crawling data sets, no need to manually check for anomalous web pages. This workflow is based on scikit-learn and PyCaret, which maps requests to symbols by exploiting regularities of frequent words or phrases that characterize unexpected queries. It version 2+. —
Unsupervised learning is about finding patterns in data that should not be there. It sounds simple, but as a computer scientist working with images I found that finding a useful and efficient strategy can be difficult. There have been many attempts at unsupervised pattern recognition over the years, including neural networks and clustering algorithms. However, they generally fail when confronted with high-dimensional datasets. In this post I discuss an approach to anomaly detection using PyCaret and scikit-learn.
Anomaly detection, also called out-of-place object detection, is used in computer vision to compare one computer-generated image with another to detect objects that are missing or have moved from their location in the original image. The process of detecting out-of-place objects (objects located outside their original location) is well studied by both artificial intelligence and machine learning researchers. This PyCaret workflow is a method for building anomaly detection models.
Anomaly Detection with PyCaret is designed to help you detect when your scripts are not working as expected. Different / unusual behavior can often signal that something has gone wrong, and you can use this script to determine what you should look at to find the problem. This script works concurrently with a Python workflow which will automatically re-run your tests to find problems, so you’ll always be alerted of an error with minimal effort.
Anomaly detection has moved from the domain of Large-scale Machine Learning databases to hundreds of applications today, and one of the challenges until now was its lack of integration. This paper begins to address this issue by integrating anomaly detection with PyCaret, an open source framework for automated workflows. Using PyCaret, anomaly detection tasks can be created directly from within the workflow for a seamless transition into automation. Furthermore, custom heuristics allow users to override default thresholds. Using our customizable heuristics pipeline, users can combine different custom constraints like years over 6 to detect Frauds in an email domain.
