boosting – Andrew Fairless, Ph.D.

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: Advances in TF-Ranking

By Andrew Fairless on August 31, 2021August 17, 2021

https://ai.googleblog.com/2021/07/advances-in-tf-ranking.html Advances in TF-RankingTuesday, July 27, 2021 Posted by Michael Bendersky and Xuanhui Wang, Software Engineers, Google Research“LTR models — unlike standard classification models that classify one item at aContinue readingWhat I Read: Advances in TF-Ranking

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: Can Model Monitor Another Model?

By Andrew Fairless on June 28, 2021June 7, 2021

https://towardsdatascience.com/can-you-build-a-machine-learning-model-to-monitor-another-model-15ad561d26df Can You Build a Machine Learning Model to Monitor Another Model?Emeli DralMay 28, 2021 “Can you train a machine learning model to predict your model’s mistakes? Nothing stops youContinue readingWhat I Read: Can Model Monitor Another Model?

What I Read: Rip van Winkle’s Razor, Adaptive Data Analysis

By Andrew Fairless on April 17, 2021April 10, 2021

http://www.offconvex.org/2021/04/07/ripvanwinkle/ Rip van Winkle’s Razor, a Simple New Estimate for Adaptive Data AnalysisSanjeev Arora, Yi ZhangApr 7, 2021 “Can you trust a model whose designer had access to the test/holdoutContinue readingWhat I Read: Rip van Winkle’s Razor, Adaptive Data Analysis

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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