How to Get the Most Out of Machine Learning
Assessing performance of machine learning algorithms
There are so many methods for measuring accuracy of performance of machine learning (ML) – you need speedy turnaround and the ability to handle data sets, and you have to overcome incomplete data and classification inaccuracies. The measure you adopt matters – so aim to define your quality metric ahead of modeling.
In his address, David Hand talks through his insights and clarity of things most relevant to effectively assessing model performance.
You’ll learn that:
- Different measures are appropriate for different questions.
- Performance is not an intrinsic property of a classifier.
- Comparative evaluations on diverse past datasets may not be relevant to your problem.
Hear from leaders in the machine learning realm from Brewer Science, Abt Associates and SAS
In this discussion, panelists share the importance of collecting high-quality data, as well as why data prep is vital in ML. You’ll also hear their take on why Design of Experiments (DOE) is transformative to the ML innovation process and what gives them motivation to look at the bigger picture of statistical problem solving.
You’ll hear from:
- Diana Ballard, Brewer Science.
- Jason Brinkley, Abt Associates.
- Jim Georges, SAS.