[TBC] Sefik Serengil | From Data to Programs: Building Tree-Based Machine Learning Models in Python

[AlexRossKnox]

Main talk (25 minutes + 5 minutes for Q&A)

Speaker: Sefik Serengil serengil@gmail.com @serengil
Title: From Data to Programs: Building Tree-Based Machine Learning Models in Python
Abstract: In this talk, we will explore the powerful and versatile ChefBoost library in Python, which enables the creation of Python programs directly from data using tree-based machine learning models. Decision trees are inherently interpretable and explainable, offering significant advantages over neural networks in terms of transparency and understanding of the decision-making process. We will delve into why tree-based models, such as decision trees, random forests, and gradient boosting machines, consistently outperform neural networks in Kaggle competitions and other data science challenges involving tabular data. Attendees will learn the practical implementation of these models, how to leverage their strengths, and the scenarios where they excel.
Recording consent: Yes
Publishing slides consent: Yes
Availability: Most probably will be out of the UK in aug, please discuss available slot first
Special requirements: No
Submitted 10/07/2024 10:30:53 via PyData London - Submit a Talk

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[AlexRossKnox]

Jiarui commented: FYI, this speaker has presented on Meetup 86, on a different topic.

Prashant commented: FYI This speaker has presented on Meetup 93 as well.