regression – Page 2 – 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: 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

What I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

By Andrew Fairless on June 7, 2022May 23, 2022

https://www.quantamagazine.org/machine-scientists-distill-the-laws-of-physics-from-raw-data-20220510/ Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw DataCharlie WoodStaff WriterMay 10, 2022 “Researchers say we’re on the cusp of “GoPro physics,” where a camera can pointContinue readingWhat I Read: ‘Machine Scientists’ Distill the Laws of Physics From Raw Data

What I Read: forecasting, quantile functions

By Andrew Fairless on May 18, 2022April 25, 2022

https://www.amazon.science/blog/improving-forecasting-by-learning-quantile-functions Improving forecasting by learning quantile functionsBy Youngsuk Park, François-Xavier AubetMarch 31, 2022 “Learning the complete quantile function, which maps probabilities to variable values, rather than building separate models forContinue readingWhat I Read: forecasting, quantile functions

What I Read: binary cross-entropy, log loss

By Andrew Fairless on October 25, 2021October 16, 2021

https://towardsdatascience.com/understanding-binary-cross-entropy-log-loss-a-visual-explanation-a3ac6025181a?gi=375ce73be21b Understanding binary cross-entropy / log loss: a visual explanationDaniel GodoyNov 21, 2018 “If you are training a binary classifier, chances are you are using binary cross-entropy / log lossContinue readingWhat I Read: binary cross-entropy, log loss

What I Read: learning-to-rank

By Andrew Fairless on October 6, 2021September 21, 2021

https://www.amazon.science/blog/using-learning-to-rank-to-precisely-locate-where-to-deliver-packages Using learning-to-rank to precisely locate where to deliver packagesModels adapted from information retrieval deal well with noisy GPS input and can leverage map information.By George FormanSeptember 15, 2021 “ForContinue readingWhat I Read: learning-to-rank

What I Read: Double Machine Learning for causal inference

By Andrew Fairless on July 21, 2021July 2, 2021

https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Double Machine Learning for causal inferenceHow Double Machine Learning for causal inference works, from the theoretical foundations to an example of application.Borja VelascoJun 25 “This post tries to explain,Continue readingWhat I Read: Double Machine Learning for causal inference

What I Read: Causal design patterns

By Andrew Fairless on April 1, 2021March 15, 2021

https://emilyriederer.netlify.app/post/causal-design-patterns/ Causal design patterns for data analystsJan 30, 2021 “…it’s easy to do causal inference wrong. For example, attempting to control for the wrong variables can sometimes induce correlations andContinue readingWhat I Read: Causal design patterns

What I Read: Structural Time Series

By Andrew Fairless on February 11, 2021January 13, 2021

https://structural-time-series.fastforwardlabs.com/Structural Time SeriesFF16 · October 2020 “We will describe a family of models—Generalized Additive Models, or GAMs—which have the advantage of being scalable and easy to interpret, and tend toContinue readingWhat I Read: Structural Time Series

Tag: regression