regression – Andrew Fairless, Ph.D.

What I Read: Contextual Bandit, LinUCB:

By Andrew Fairless on November 5, 2024August 15, 2024

https://truetheta.io/concepts/reinforcement-learning/lin-ucb A Reliable Contextual Bandit Algorithm: LinUCBDJ RichAugust 6, 2024 “A user visits a news website. Which articles should they be shown?”

What I Read: Logarithms, Heteroskedasticity

By Andrew Fairless on August 29, 2024June 15, 2024

https://allendowney.substack.com/p/logarithms-and-heteroskedasticity Logarithms and HeteroskedasticityAllen DowneyMay 26, 2024 “A log transform is neither necessary nor sufficient to fix heteroskedasticity”

What I Read: Generalized Additive Models

By Andrew Fairless on June 13, 2024April 15, 2024

https://ecogambler.netlify.app/blog/interpreting-gams How to interpret and report nonlinear effects from Generalized Additive ModelsNicholas ClarkMarch 21, 2024 “Generalized Additive Models (GAMs)… are helpful for learning arbitrarily complex, nonlinear relationships between predictors andContinue readingWhat I Read: Generalized Additive Models

What I Read: polynomial monster

By Andrew Fairless on March 26, 2024February 5, 2024

https://alexshtf.github.io/2024/01/25/Bernstein-Basis.html Keeping the polynomial monster under controlAlex ShtoffJan 25, 2024 “…we saw that the Bernstein polynomials can be used to fit a high-degree polynomial curve with ease, without its shapeContinue readingWhat I Read: polynomial monster

What I Read: polynomial features

By Andrew Fairless on March 25, 2024February 5, 2024

https://alexshtf.github.io/2024/01/21/Bernstein.html Are polynomial features the root of all evil?Alex ShtoffJan 21, 2024 “There’s nothing inherently wrong with high degree polynomials, and in contrast to what is typically taught, high degreeContinue readingWhat I Read: polynomial features

What I Read: Multiple Imputation by Chained Equations

By Andrew Fairless on September 20, 2023September 4, 2023

https://cameronpatrick.com/post/2023/07/mice-in-a-blog-post/ Dirty imputation done dirt cheap: implementing Multiple Imputation by Chained Equations in one blog postCameron Patrick31 July, 2023 “An attempt to understand in detail how Multiple Imputation by ChainedContinue readingWhat I Read: Multiple Imputation by Chained Equations

What I Read: different meanings of p-value

By Andrew Fairless on June 7, 2023May 1, 2023

4 different meanings of p-value (and how my thinking has changed) 4 different meanings of p-value (and how my thinking has changed)Posted on April 14, 2023 9:14 AMAndrew Gelman “TheContinue readingWhat I Read: different meanings of p-value

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: Concept Drift Without Labeled Data

By Andrew Fairless on September 26, 2022August 22, 2022

https://concept-drift.fastforwardlabs.com/ Inferring Concept Drift Without Labeled DataFF22Aug 2021 “…we explore broadly applicable approaches for dealing with concept drift when labeled data is not readily accessible. We’ll start by defining whatContinue readingWhat I Read: Concept Drift Without Labeled Data

What I Read: introduction to NumPyro

By Andrew Fairless on September 19, 2022August 22, 2022

https://dfm.io/posts/intro-to-numpyro/ An astronomer’s introduction to NumPyroDan Foreman-MackeyJul 28 2022 “In this post… I’ll focus primarily on providing an introduction to NumPyro, which is a probabilistic programming library that provides anContinue readingWhat I Read: introduction to NumPyro

Tag: regression