5, 10 or 20 seats+ for your team - learn more
Red alert! One of the energy production companies managed by Sigma Corp has suffered an outage. An investigation has led to the conclusion that the facility’s anomaly detection mechanism failed to detect early signals due to a sudden change in the distribution of the analyzed data. As a lead data scientist at Sigma, you’ll build an Isolation Forest algorithm, which is less likely than the Empirical Cumulative distribution-based Outlier Detection (ECOD) method to fail in such scenarios. To gauge how robust your method is, you’ll benchmark your algorithms against adversarial scenarios, synthetic anomalies, and standard datasets. When you’re done, you’ll have practical experience creating, using, and testing the Isolation Forest algorithm as an effective alternative to ECOD in circumstances where the data distribution changes.
This liveProject is for beginner data scientists who want to learn how to build an anomaly detection algorithm for multidimensional data that is effective even when the distribution of data changes. To begin these liveProjects you’ll need to be familiar with the following:TOOLS
In this liveProject, you’ll learn to build and test the Isolation Forest algorithm as an effective alternative to ECOD in scenarios where data distribution changes.
geekle is based on a wordle clone.