decision tree – Andrew Fairless, Ph.D.

What I Read: Multi Objective Optimisation

By Andrew Fairless on February 6, 2025October 25, 2024

https://blog.flipkart.tech/multi-objective-optimisation-in-suggestions-ranking-flipkart-49099b951eae?gi=04415d605535 Multi Objective Optimisation in Suggestions Ranking @ FlipkartPranjal SanjanwalaApr 19, 2024 “…we aim to provide a perfectly tailored set of suggestions for that user at that point in time.Continue readingWhat I Read: Multi Objective Optimisation

What I Read: Classifying pdfs

By Andrew Fairless on November 21, 2024August 26, 2024

https://snats.xyz/pages/articles/classifying_a_bunch_of_pdfs.html Classifying all of the pdfs on the internetSantiago Pedroza2024-08-18 “How would you classify all the pdfs in the internet? Well, that is what I tried doing this time.”

What I Read: Random Forests, Estimation of Information-Theoretic Quantities

By Andrew Fairless on February 27, 2024January 25, 2024

https://arxiv.org/abs/1907.00325v7 Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic QuantitiesRonan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell, Hayden Helm, Cencheng Shen, Joshua T. Vogelstein “WeContinue readingWhat I Read: Random Forests, Estimation of Information-Theoretic Quantities

What I Read: SAT Solvers

By Andrew Fairless on January 8, 2024November 7, 2023

https://www.borealisai.com/research-blogs/tutorial-9-sat-solvers-i-introduction-and-applications/ Tutorial #9: SAT Solvers I: Introduction and applicationsS. PrinceNov. 10, 2020 “This tutorial concerns the Boolean satisfiability or SAT problem…. we’ll show how to fit both neural networks andContinue readingWhat I Read: SAT Solvers

What I Read: Competitive Machine Learning

By Andrew Fairless on May 9, 2023March 30, 2023

https://mlcontests.com/state-of-competitive-machine-learning-2022/ The State of Competitive Machine Learning2022 Edition “We summarise the state of the competitive landscape and analyse the 200+ competitions that took place in 2022. Plus a deep diveContinue readingWhat I Read: Competitive Machine Learning

What I Read: Predict Distributions

By Andrew Fairless on April 17, 2023March 30, 2023

https://towardsdatascience.com/a-new-way-to-predict-probability-distributions-e7258349f464?gi=3201d118e909 A New Way to Predict Probability DistributionsExploring multi-quantile regression with CatboostHarrison HoffmanFeb 14 “Until recently, the main disadvantage of quantile regression was that one model had to be trainedContinue readingWhat I Read: Predict Distributions

What I Read: XGBoost, Order Does Matter

By Andrew Fairless on September 13, 2021August 20, 2021

https://towardsdatascience.com/xgboost-order-does-matter-60d8d0d5aa71?gi=d051128bfa6b XGBoost: Order Does MatterWhat impact does the order of the features have on XGBoost models, and how can you use feature importance to identify related features in your data?BityaContinue readingWhat I Read: XGBoost, Order Does Matter

What I Read: Tabular Data, Deep Learning is Not All You Need

By Andrew Fairless on July 14, 2021June 22, 2021

https://arxiv.org/abs/2106.03253v1 Tabular Data: Deep Learning is Not All You NeedRavid Shwartz-Ziv, Amitai Armon “Our study shows that XGBoost outperforms these deep models across the datasets, including datasets used in theContinue readingWhat I Read: Tabular Data, Deep Learning is Not All You Need

What I Read: Comprehensive Guide to Ensemble Learning

By Andrew Fairless on June 29, 2021June 7, 2021

https://www.kdnuggets.com/2021/05/comprehensive-guide-ensemble-learning.html A Comprehensive Guide to Ensemble Learning – Exactly What You Need to KnowThis article covers ensemble learning methods, and exactly what you need to know in order to understandContinue readingWhat I Read: Comprehensive Guide to Ensemble Learning

What I Read: XGBoost, What it is

By Andrew Fairless on February 27, 2021February 18, 2021

https://www.kdnuggets.com/2020/12/xgboost-what-when.html XGBoost: What it is, and when to use itBy Harish Krishna, Praxis Business School “XGBoost is a tree based ensemble machine learning algorithm…”

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