I spoke with Antonie Berkel and Dr. Joost Huiskens about a systems analysis project for the design of a clinical decision support system (CDSS) which integrates computer vision to help radiologists monitor the progression of tumors.
In the preceding two posts, we looked at issues around interpretability of modern black-box machine-learning models and introduced SAS® Model Studio within SAS® Visual Data Mining and Machine Learning. Now we turn our attention to programmatic interpretability.
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.
A monotonic relationship exists when a model’s output increases or stays constant in step with an increase in your model’s inputs. Relationships can be monotonically increasing or decreasing with the distinction based on which direction the input and output travel. A common example is in credit risk where you would expect someone’s risk score to increase with the amount of debt they have relative to their income.
We will use prescriptive analytics and optimization to select a stock portfolio that maximizes returns while taking risk tolerance into account.
Developing a loan approval application is a sensitive task since automatically approving loans to customers who will default can be costly for a lender. SAS enables quick and easy development and exposure of decision-making models from a single source. A simple and robust environment can make decisions less prone to errors.
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.
Ever since automated machine learning has entered the scene, people are asking, "Will automated machine learning replace data scientists?"
Let's talk about using DLPy to model employee retention through a survival analysis model. Survival analysis is used to model time-to-event. Examples of time-to-event include the time until an employee leaves a company, the time until a disease goes into remission, or the time until a mechanical part fails. The
Through hyperparameter autotuning, you can maximize a model's performance without maximizing effort. While SAS searches the hyperparameter space in the background, you are free to pursue other work.
