PyData Tel Aviv Meetup: SHAP Values for ML Explainability - Adi Watzman
PyData Tel Aviv Meetup #28
2 January 2020
Sponsored and Hosted by PayPal
https://www.meetup.com/PyData-Tel-Aviv/
How do ML models use their features to make predictions?
SHAP opens up the ML black box by providing feature attributions for every prediction of every model. Being a relatively new method (arxiv.org/abs/[masked]) , SHAP is gaining popularity extremely quickly thanks to its user-friendly API and theoretical guarantees.
In this talk I will guide your intuition through the exciting theory SHAP is based on, and demonstrate how SHAP values can be aggregated to understand model behavior. Throughout the talk I will present real-life examples for using SHAP in the fraud detection domain at PayPal, and in the medical domain as provided by the SHAP authors’.
About the speaker:
Adi is a data scientist at PayPal, developing Machine Learning models for fraud detection. She also volunteers in The public Knowledge Workshop (הסדנה לידע ציבורי), applying data science to improve public transportation in Israel. Adi loves data and algorithms, and is extremely excited about explaining machine learning models.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
Find a PyData chapter near you: meetup.com/pro/pydata 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
Видео PyData Tel Aviv Meetup: SHAP Values for ML Explainability - Adi Watzman канала PyData
2 January 2020
Sponsored and Hosted by PayPal
https://www.meetup.com/PyData-Tel-Aviv/
How do ML models use their features to make predictions?
SHAP opens up the ML black box by providing feature attributions for every prediction of every model. Being a relatively new method (arxiv.org/abs/[masked]) , SHAP is gaining popularity extremely quickly thanks to its user-friendly API and theoretical guarantees.
In this talk I will guide your intuition through the exciting theory SHAP is based on, and demonstrate how SHAP values can be aggregated to understand model behavior. Throughout the talk I will present real-life examples for using SHAP in the fraud detection domain at PayPal, and in the medical domain as provided by the SHAP authors’.
About the speaker:
Adi is a data scientist at PayPal, developing Machine Learning models for fraud detection. She also volunteers in The public Knowledge Workshop (הסדנה לידע ציבורי), applying data science to improve public transportation in Israel. Adi loves data and algorithms, and is extremely excited about explaining machine learning models.
www.pydata.org
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
Find a PyData chapter near you: meetup.com/pro/pydata 00:00 Welcome!
00:10 Help us add time stamps or captions to this video! See the description for details.
Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps
Видео PyData Tel Aviv Meetup: SHAP Values for ML Explainability - Adi Watzman канала PyData
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