This is part of Machine Learning-Driven Anomaly Detection
What you will learn
For a model built with historical data, model drift or concept drift occurs when there is a significant change in the model behavior with the passage of time due to the nonstationarity of data
Concept drift can be of the following types with respect to classification problems
- Change p(y | x) i.e the class boundary changes without change in p(x). It’s called real drift. The trained model dictates p(y|x)
- Change in p(x) only called virtual drift
- Change in p(x) and p(y|x)
Drift Algorithms we will cover:
- Drift detection method (DDM)
- Adaptive windowing (ADWIN)
- Early drift detection method (EDDM)
- Exponentially weighted moving average concept drift detection (ECDD)
- Local drift detection (LLDD)
- Kullback Leibler (KL) divergence
- Kolmogorov Smirnov test (KS)
- Cramer Von Mises test (CVM)
- And many more.
This is a FREE class!
Can’t make it to live session? No worries. Go ahead and register; we will send you the session recording.
See below for past session recordings & notes
COO, CIO, DevOps, Software Engineers
- Must have: Development experience
- Nice to have: Python knowledge
What to Bring
- Please bring a reasonably modern laptop (Corporate laptops with overly restrictive firewalls may not work well; Personal laptops are recommended)
- [nice to have] download our docker image elephantscale/es-training
- Class Notes here
Pranab Ghosh is a Data Science Consultant, He owns several open-source Big Data and Data Science projects using Hadoop, Spark, Storm, Kafka, NoSQL databases, and the related ecosystem.