Feature selection is the task of finding relevant features used in a machine learning model. Often used for this task are models which produce a sparse subset of all input features by permitting the use of additional features (e.g. Lasso with L1 regularization). But these models are often tuned to filter out redundancies in the input set and produce only an unstable solution especially in the presence of higher dimensional data.
FRI calculates relevance bound values for all input features. These bounds give rise to intervals which we named ‘feature relevance intervals’ (FRI). A given interval symbolizes the allowed contribution each feature has, when it is allowed to be maximized and minimized independently from the others. This allows us to approximate the global solution instead of relying on the local solutions of the alternatives.
Lukas Pfannschmidt
Sr. Machine Learning Engineer
My expertise now encompasses advanced areas in machine learning and backend system optimization. My work in backend optimization, particularly in managing large-scale data efficiently, aligns with key optimization metrics like cost, quality, and speed. I also contribute to improving overall system reliability and observability, significantly reducing error rates and establishing critical technical metrics. These endeavors complement my previous research interests in high-performance computing.
