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.
We have updated our software for improved interpretability since this post was written. For the latest on this topic, read our new series on model-agnostic interpretability. 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
We have updated our software for improved interpretability since this post was written. For the latest on this topic, read our new series on model-agnostic interpretability. Don`t jump into modelling. First, understand and explore your data! This is common advice for many data scientists. If your data set is messy,
We have updated our software for improved interpretability since this post was written. For the latest on this topic, read our new series on model-agnostic interpretability. As machine learning takes its place in many recent advances in science and technology, the interpretability of machine learning models grows in importance. We
When you go to the grocery store, you see that items of a similar nature are displayed nearby to each other. When you organize the clothes in your closet, you put similar items together (e.g. shirts in one section, pants in another). Every personal organizing tip on the web to
