There’s a fundamental problem with the typical machine learning model-development process: It evaluates models in terms of technical metrics like AUC, precision, or F-score, rather than business metrics like profit or savings - the stuff that actually matters to the company.
There’s a fundamental problem with the typical machine learning model-development process: It evaluates models in terms of technical metrics like AUC, precision, or F-score, rather than business metrics like profit or savings - the stuff that actually matters to the company.
Speaker: Eric Siegel | View Anytime | Duration: 60 Minutes | Price: ¤190.00 | View Details