xgboost

Dive into this episode of The AI Frontier podcast, where we explore Ensemble Learning techniques like Boosting, Bagging, and Random Forests in Machine Learning. Learn about their applications, advantages, and limitations, and discover real-world success stories. Enhance your understanding of these powerful methods and stay ahead in the world of data science.

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Unlock the power of XGBoost by learning how to fine-tune its hyperparameters and discover its optimal modeling situations. This and more, when best-selling author and leading Python consultant Matt Harrison teams up with Jon Krohn for yet another jam-packed technical episode! Are you ready to upgrade your data science toolkit in just one hour? Tune-in now!

This episode is brought to you by Pathway, the reactive data processing framework, by Posit, the open-source data science company, and by Anaconda, the world's most popular Python distribution. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• Matt's book ‘Effective XGBoost’ [07:05]
• What is XGBoost [09:09]
• XGBoost's key model hyperparameters [19:01]
• XGBoost's secret sauce [29:57]
• When to use XGBoost [34:45]
• When not to use XGBoost [41:42]
• Matt’s recommended Python libraries [47:36]
• Matt's production tips [57:57]

Additional materials: www.superdatascience.com/681

Emmanuel Turlay spent more than a decade in engineering roles at tech-first companies like Instacart and Cruise before realizing machine learning engineers need a better solution. Emmanuel started Sematic earlier this year and was part of the YC summer 2022 batch. He recently raised a $3M seed round from investors including Race Capital and Soma Capital. Thanks to friend of the podcast and former guest Hina Dixit from Samsung NEXT for the intro to Emmanuel.

I’ve been involved with the AutoML space for five years and, for full disclosure, I’m on the board of Auger which is in a related space. I’ve seen the space evolve and know how much room there is for innovation. This one's a great education about what’s broken and what’s ahead from a true machine learning pioneer.

Listen and learn...

1. How to turn every software engineer into a machine learning engineer
2. How AutoML platforms are automating tasks performed in traditional ML tools
3. How Emmanuel translated learning from Cruise, the self-driving car company, into an open source platform available to all data engineering teams
4. How to move from building an ML model locally to deploying it to the cloud and creating a data pipeline... in hours
5. What you should know about self-driving cars... from one of the experts who developed the brains that power them
6. Why 80% of AI and ML projects fail

References in this episode:

• Unscrupulous users manipulate LLMs to spew hate
• Hina Dixit from Samsung NEXT on AI and the Future of Work
• Apache Beam
• Eliot Shmukler, Anomalo CEO, on AI and the Future of Work

The Surfers explore the impact that NASHmap, the first Machine Learning model that can identify patients likely to have NASH, can have on clinical trials and patient treatment.

Prof. Schattenberg and colleagues built the NASHmap model from a NIDDK database and validated it using the Optum de-identified EHR dataset. The model includes 14 variables, some obvious, others less so, that produce an AUC of 0.82 in the NIDDK database and 0.79 in the Optum database. This conversation explores NASHmap's powerful implications for future clinical trial recruitment and large-scale patient screening.

The Surfers analyze outputs from NASHmap, the first Machine Learning model that can identify patients likely to have NASH, to learn what we can about the disease and how to think about its value for racial and ethnic minorities.

Prof. Schattenberg and colleagues built the NASHmap model from a NIDDK database and validated it using the Optum de-identified EHR dataset. The model includes 14 variables, some obvious, others less so, that produce an AUC of 0.82 in the NIDDK database and 0.79 in the Optum database. This conversation explores some model elements and statistical outputs to glean knowledge about what the model says about NASH itself.

Jörn Schattenberg discusses NASHmap, the first Machine Learning model that can identify patients likely to have NASH in clinical settings. Louise, Roger and guest Dr. Kris Kowdley join Jörn to discuss the model from academic, patient treatment and statistical perspectives.

Prof. Schattenberg and colleagues built the NASHmap model from a NIDDK database and validated it using the Optum de-identified EHR dataset. The model includes 14 variables, some obvious, others less so, that produce an AUC of 0.82 in the NIDDK database and 0.79 in the Optum database. The episode focuses on how the model was built and what it implies for clinical trial recruitment, patient care and the view it provides of NASH risk factors. This has powerful implications for future clinical trial recruitment and large-scale patient screening. 

Jörn Schattenberg discusses the issues that drove the project to develop a Machine Learning model that can identify patients likely to have NASH in clinical settings, and discusses key elements of study and model design.

Prof. Schattenberg and colleagues built the NASHmap model from a NIDDK database and validated it using the Optum de-identified EHR dataset. The model includes 14 variables, some obvious, others less so, that produce an AUC of 0.82 in the NIDDK database and 0.79 in the Optum database. This conversation focuses on why the collaborators undertook this project and how the model was built.