Concept drift can occur when the relationship between the input and output data changes i.e P(Y|X) changes while P(X) remains same. It happens when the behavior of the underlaying process has changed with respect to the model training time. This situation is not uncommon in real world situations, especially when it comes to ML models that involve human behavior, explicitly or implicitly. Please refer to my earlier post for more technical information on concept drift. I had a batch based implementation of several model drift detection algorithm as described in my earlier post. I implemented window based real time API for concept drift detection made them part of my Python package matumizi. The code is in my GitHub repo whakapai.
Continue reading-
Recent Posts
Top Posts
- Meeting Schedule Optimization with Genetic Algorithm in Python
- Simulating A/B Test with Counterfactual and Machine Learning Regression Model
- Realtime Concept Drift Detection for Machine Learning Classification Models
- Conformal Prediction for a Neural Regression Model
- Information Gain based Feature Selection in Python for Machine Learning Models
- Storing Nested Objects in Cassandra with Composite Columns
- Improving Elastic Search Query Result with Query Expansion using Topic Modeling
- Concept Drift Detection Techniques with Python Implementation for Supervised Machine Learning Models
- AI Past, Present and Future
- A Learning but Greedy Gambler
Archives
- May 2023
- April 2023
- March 2023
- February 2023
- January 2023
- December 2022
- November 2022
- October 2022
- September 2022
- August 2022
- July 2022
- June 2022
- May 2022
- April 2022
- March 2022
- February 2022
- January 2022
- December 2021
- November 2021
- October 2021
- September 2021
- August 2021
- July 2021
- June 2021
- May 2021
- April 2021
- March 2021
- February 2021
- January 2021
- December 2020
- November 2020
- October 2020
- September 2020
- August 2020
- July 2020
- June 2020
- May 2020
- April 2020
- March 2020
- February 2020
- January 2020
- December 2019
- November 2019
- October 2019
- September 2019
- August 2019
- July 2019
- June 2019
- May 2019
- April 2019
- March 2019
- February 2019
- January 2019
- December 2018
- November 2018
- October 2018
- September 2018
- August 2018
- July 2018
- June 2018
- May 2018
- April 2018
- March 2018
- February 2018
- January 2018
- December 2017
- November 2017
- October 2017
- September 2017
- August 2017
- July 2017
- June 2017
- May 2017
- April 2017
- March 2017
- February 2017
- January 2017
- December 2016
- November 2016
- October 2016
- September 2016
- August 2016
- July 2016
- June 2016
- May 2016
- April 2016
- March 2016
- February 2016
- January 2016
- December 2015
- November 2015
- October 2015
- September 2015
- August 2015
- July 2015
- June 2015
- May 2015
- April 2015
- March 2015
- February 2015
- January 2015
- December 2014
- November 2014
- October 2014
- September 2014
- August 2014
- July 2014
- June 2014
- May 2014
- April 2014
- March 2014
- February 2014
- January 2014
- December 2013
- November 2013
- October 2013
- September 2013
- August 2013
- July 2013
- June 2013
- May 2013
- April 2013
- March 2013
- February 2013
- January 2013
- December 2012
- November 2012
- October 2012
- September 2012
- August 2012
- July 2012
- June 2012
- May 2012
- April 2012
- March 2012
- February 2012
- January 2012
- December 2011
- November 2011
- October 2011
- August 2011
- July 2011
- June 2011
- May 2011
- April 2011
- March 2011
- January 2011
- December 2010
- November 2010
- October 2010
- September 2010
- August 2010
Categories
- AI
- Anomaly Detection
- Approximate Query
- Association Mining
- Big Data
- BPM
- Cassandra
- causality
- Cluster Computation
- Collaborative Filtering
- Correlation
- Customer Service
- Data Mining
- Data Model
- Data Profiling
- data quality
- Data Science
- Data Transformation
- Data Warehouse
- Deep Learning
- eCommerce
- elastic search
- ETL
- Fraud Detection
- Hadoop and Map Reduce
- HBase
- Healthcare Analytic
- Hive
- Indexing
- Internet of Things
- Java
- Key Value Store
- Log Analysis
- Machine Learning
- Map Reduce
- Marketing Analytic
- Messaging
- mlops
- Mobile
- MongoDB
- multi arm bandit
- NLP
- NOSQL
- Optimizatiom
- Optimization
- Outlier Detection
- Performance
- Personalization
- Predictive Analytic
- Programing Language
- Python
- PyTorch
- Query
- Real Time Processing
- Recommendation Engine
- Redis
- Reinforcement Learning
- Ruby
- Rule Engine
- Rule Mining
- Scala
- ScikitLearn
- Search
- Search Analytic
- Semantic
- Simulation
- Solr
- Spark
- Spark Streaming
- Statistics
- Storm
- stream processing
- Supervised Learning
- TensorFlow
- Text Analytic
- Text Mining
- time series
- Time Series Analytic
- Topic Modeling
- Uncategorized
- Web
- Web Analytic
- Workflow
Meta
- AI Anomaly Detection Big Data Cassandra Collaborative Filtering Data Mining Data Science Deep Learning eCommerce ETL Hadoop and Map Reduce Java Machine Learning Map Reduce Marketing Analytic NOSQL Optimization Outlier Detection Predictive Analytic Python PyTorch Real Time Processing Recommendation Engine Reinforcement Learning Scala Spark Statistics Storm Time Series Analytic Uncategorized
- alarm flooding
- anomaly detection
- big data
- bloat
- Cassandra
- Cassndra
- causal inference
- clustering
- concept drift
- counterfactual
- CRM
- customer churn
- customer conversion
- customer loyalty
- customer segmentation
- Customer Service
- data lake
- data mining
- Data model
- data quality
- data transformation
- data validation
- decision tree
- dedup
- eCommerce
- entropy
- ETL
- feature selection
- fraud
- generalization error
- gini index
- Hadoop
- HBase
- HDFS
- high cardinality
- Hive
- Index
- IoT
- JSON
- map reduce
- marketing campaign
- markov chain
- mlops
- mobile
- model complexity
- model robustness
- MongoDB
- monte carlo simulation
- multi arm bandit
- nearest neighbor
- NOSQL
- outlier
- outlier detection
- price optimization
- programing language
- Query
- random forest
- real time
- recommendation
- recommendation engine
- regression
- retail
- scikit
- scikit-learn
- seasonality
- similarity
- simulated annealing
- Solr
- spark
- stochastic optimization
- stream processing
- supply chain
- synthetic data
- time series
- Web log mining