))
Debate: One Metric to Rule Them All? AUC for Classification Models
Manage episode 407983676 series 3338095
Το περιεχόμενο παρέχεται από το Elder Research. Όλο το περιεχόμενο podcast, συμπεριλαμβανομένων των επεισοδίων, των γραφικών και των περιγραφών podcast, μεταφορτώνεται και παρέχεται απευθείας από τον Elder Research ή τον συνεργάτη της πλατφόρμας podcast. Εάν πιστεύετε ότι κάποιος χρησιμοποιεί το έργο σας που προστατεύεται από πνευματικά δικαιώματα χωρίς την άδειά σας, μπορείτε να ακολουθήσετε τη διαδικασία που περιγράφεται εδώ https://el.player.fm/legal.
When considering evaluation metrics for classification models, is it possible for one metric to rule them all? Join us for a lively debate between Aric LaBarr, Associate Professor of Analytics at NC State's Institute for Advanced Analytics, and Robert Robison, Elder Research Senior Data Scientist.
During the debate Robert champions AUC’s comprehensive measure of model performance, while Aric advocates for a broader perspective, emphasizing the importance of business context in metric selection. Tune in as host Evan Wimpey moderates the discussion, and gain valuable insight on what really matters when it comes to machine learning model evaluation. We hope you enjoy the conversation!
In this episode you will learn:
⛛ The importance of exploring various metrics to evaluate model performance
⛛ Why metrics should align with business objectives
⛛ The need for data science teams to invest time in feature engineering
⛛ Why a model's success relies not only on its performance but also on stakeholders' ability to understand and trust the insights it provides
Quotes
💬 “There's a difference between communicating the value of a model and distinguishing between which models are better.” –Robert Robison
💬 “If you can't explain the model to your stakeholders or business users, then it's not going to get implemented.” –Aric LaBarr
Featured in This Episode
Aric LaBarr | Associate Professor of Analytics, Institute for Advanced Analytics
LinkedIn: https://www.linkedin.com/in/ariclabarr/
Robert Robison | Senior Data Scientist, Elder Research
LinkedIn: https://www.linkedin.com/in/robert-robison/
Evan Wimpey, Director of Analytics Strategy, Elder Research
LinkedIn: linkedin.com/in/evan-wimpey
Chapters
00:00 Evan introduces the debate topic and guests, Aric LaBarr and Robert Robison.
01:37 Robert begins his argument by defining AUC (Area Under the Curve) and its significance as a metric for classification models.
06:11 Aric begins his rebuttal, challenging the notion that AUC is the only metric to consider.
09:26 Robert provides a rebuttal to Aric's points.
11:48 Aric starts his rebuttal, focusing on communicating models to business users.
14:41 Robert responds to Aric’s points.
16:18 Evan asks Robert if certain cases may require metrics other than AUC.
17:03 Robert responds to Evan’s question.
17:53 Aric weighs in on the question.
19:37 Evan asks Aric if focusing solely on AUC may save time and costs.
20:30 Aric responds to Evan’s question.
22:20 Evan gives time for the debaters to ask each other questions.
25:30 The debaters share closing remarks, summarizing their positions.
29:14 Evan wraps up the show.
Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/
35 επεισόδια
Μοίρασέ το
