Erik Drysdale

In this post I describe how to calculate the first and second moments of a loss function for a machine learning (ML) model.

Most machine learning models are point predictors: they output a single number (a regression estimate or a most-likely class label).

This post describes how to adjust the traditional sample standard deviation (SD) estimator to reduce its bias.

This post accompanies the release of the sntn package, which implements a scipy-like class for doing inference on a sum of a normal and a trunctated normal (SNTN) distribution (see Kim (2006) and Arnold (1993)).

Executive summary Embeddings from the ESMFold model can be used to predict clinical outcomes for different Cystic Fibrosis (CF) genotypes found in the CFTR2 database.

Executive summary I developed a set of computational approaches to help a human (myself) write univocalic poems in the style of Christian Bök’s Eunoia.

Executive summary I developed four fine-tuned versions of OpenAI’s GPT-3 models to sound like Russ Roberts who is the host of the podcast EconTalk, using more than 10 years of episode transcripts.

This posts outlines the paranet package, which allows for the fitting of elastic net regularized parametric survival models with right-censored time-to-event data in python.

This post summarizes a newly released python package: trialML.

This post summarizes a newly released python package: SurvSet the first ever open-source time-to-event dataset repository.