Author

Ilknur Kaynar Kabul
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Sr Manager, Advanced Analytics R&D

Ilknur Kaynar-Kabul is a Senior Manager in the SAS Advanced Analytics division, where she leads the SAS R&D team that focuses on machine learning algorithms and applications. The team is responsible for researching and implementing new data mining and machine learning algorithms that can solve complex big data problems in the high-performance analytics environment. She likes working at the interface of computer science, statistics and optimization. Her research interests include model interpretability, transfer learning, clustering and feature engineering. Prior to joining SAS, Kaynar-Kabul worked on medical image analysis and visualization techniques at University of North Carolina at Chapel Hill and Kitware. She holds multiple patents in automated market segmentation using clustering and deep neural networks. She has a PhD in Computer Science from UNC Chapel Hill.

Advanced Analytics | Artificial Intelligence | Machine Learning
Ilknur Kaynar Kabul 0
Interpret model predictions with partial dependence and individual conditional expectation plots

Continuing our series on model interpretability, this post explains two methods for plotting variables that can give insight into how a model is working. Assessing a model`s accuracy usually is not enough for a data scientist who wants to know more about how a model is working. Often data scientists

Advanced Analytics | Artificial Intelligence | Machine Learning
Ilknur Kaynar Kabul 0
Interpretability is crucial for trusting AI and machine learning

As machine learning takes its place in many recent advances in science and technology, the interpretability of machine learning models grows in importance. We are surrounded with applications powered by machine learning, and we’re personally affected by the decisions made by machines more and more every day. From the mundane