Better than Deep Learning: Gradient Boosting Machines (GBM)

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With all the hype about deep learning and "AI", it is not well publicized that for structured/tabular data widely encountered in business applications it is actually another machine learning algorithm, the gradient boosting machine (GBM) that most often achieves the highest accuracy in supervised learning tasks. In this talk we'll review some of the main GBM implementations available as R and Python packages such as xgboost, h2o, lightgbm etc, we'll discuss some of their main features and characteristics, and we'll see how tuning GBMs and creating ensembles of the best models can achieve fantastic prediction accuracy for many business problems. With all the hype about deep learning and "AI", it is not well publicized that for structured/tabular data widely encountered in business applications it is actually another machine learning algorithm, the gradient boosting machine (GBM) that most often achieves the highest accuracy in supervised learning tasks. In this talk we'll review some of the main GBM implementations available as R and Python packages such as xgboost, h2o, lightgbm etc, we'll discuss some of their main features and characteristics, and we'll see how tuning GBMs and creating ensembles of the best models can achieve fantastic prediction accuracy for many business problems.

Better than Deep Learning:
Gradient Boosting Machines (GBM)
Szilard Pafka, PhD
Chief Scientist, Epoch (USA)
DataWorks Summit, Barcelona, Spain
March 2019

Disclaimer:
I am not representing my employer (Epoch) in this talk
I cannot confirm nor deny if Epoch is using any of the methods, tools,
results etc. mentioned in this talk

Source: https://twitter.com/iamdevloper/

http://www.cs.cornell.edu/~alexn/papers/empirical.icml06.pdf
http://lowrank.net/nikos/pubs/empirical.pdf

I usually use other people’s code [...] I can find open source code for
what I want to do, and my time is much better spent doing research
and feature engineering -- Owen Zhang

http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf

http://www.argmin.net/2016/06/20/hypertuning/

Better than Deep Learning: Gradient Boosting Machines (GBM)

Field notes from my experience with a better way to build software -- as practiced at Menlo Innovations in Ann Arbor Michigan through the practice of High-tech Anthropology(TM) and Agile/ XP development in the Menlo Software Factory (TM). Presented on January 6, 2009 to Berliner Bibliothekswissenschaftliches Kolloquium (BBK) (http://www.ibi.hu-berlin.de/institut/veranstaltungen/bbk/bbk-archiv). BBK is a seminar under the auspices of the Institut für Bibliotheks- und Informationswissenschaft (IBI) at Humboldt University in Belrin (http://www.ibi.hu-berlin.de/).

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Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs. 1. Unifying Large Language Models and Knowledge Graphs: A Roadmap. https://arxiv.org/abs/2306.08302 2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs: https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/

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GraphRAG is All You need? LLM & Knowledge Graph