decision tree – Andrew Fairless, Ph.D.

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…”

What I Read: Isolation Forest

By Andrew Fairless on February 16, 2021January 18, 2021

https://towardsdatascience.com/isolation-forest-is-the-best-anomaly-detection-algorithm-for-big-data-right-now-e1a18ec0f94f Isolation Forest is the best Anomaly Detection Algorithm for Big Data Right NowAndrew YoungNov 13 “Isolation forest or “iForest” is an astoundingly beautiful and elegantly simple algorithm that identifiesContinue readingWhat I Read: Isolation Forest

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