Funda works for SAS Research and Development supporting software functionalities that enable users to perform end-to-end analytics. This includes data preprocessing, feature engineering, model training/development, model assessing, model stacking, automated machine learning pipelines, and interpretable machine learning. She loves using SAS software for solving complicated predictive modeling problems (including fraud detection, COVID-19 forecasting, and health care model interpretability) by using traditional machine learning, deep learning, and natural language processing techniques. She has presented her work at major machine learning and analytics conferences, written technical papers and blog posts, and created short videos. She is a life-long learner, always looking for opportunities to learn, share and mentor. Funda has a Ph.D. in Statistics from North Carolina State University and is a co-founder of NC Women in Machine Learning and Data Science.
In the second of a three-part series of posts, SAS' Funda Gunes and her colleague Ricky Tharrington summarize model-agnostic model interpretability in SAS Viya.
In the first of a three-part series of posts, SAS' Funda Gunes and her colleague Ricky Tharrington summarize model-agnostic model interpretability in SAS Viya.
In machine learning, a feature is another word for an attribute or input, or an independent variable. What is feature engineering? Feature engineering is a process of preparing inputs for machine learning models. The goal of feature engineering is to to improve classification accuracy by considering the limitations of the
Ensemble methods are commonly used to boost predictive accuracy by combining the predictions of multiple machine learning models. The traditional wisdom has been to combine so-called “weak” learners. However, a more modern approach is to create an ensemble of a well-chosen collection of strong yet diverse models. Building powerful ensemble models
When you work with big data, you often deal with both a large number of observations and a large number of features. When the number of features is large, they can be highly correlated, resulting in significant amount of redundancy in the data. Principal component analysis can be a very
