Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles

SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.

Corresponding code guide to the tutorial paper "Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures" (Hoffman et al., 2023)

R functions for project setup, data cleaning, machine learning, SuperLearner, parallelization, and targeted learning.

Workshop (2-6 hours): cleaning, missing value imputation, EDA, ensemble learning, calibration, variable importance ranking, accumulated local effect plots. WIP.

Ensemble feature ranking for SuperLearner variable selection

Implementing Gradient Boosting , XGboost, AdaBoost & SuperLearner in Python and comparing their classification accuracy of the two methods.

A collection of additional screening algorithms for SuperLearner

SuperLearner R package: prediction model ensembling method

R code for evaluating adult HIV incidence, health, & implementation outcomes for the first phase of the SEARCH Study (https://www.searchendaids.com/). Full statistical analysis plan available at https://arxiv.org/abs/1808.03231

Introduction to Double Robust Estimation for Causal Inference

A parallel implementation of the Super Learner estimator in Python. Winner of the Statistical Learning course contest!

Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.

Hack Aotearoa 2020

npRR: Model-robust inference for the conditional relative risk function using targeted machine learning

Ensembled Feature Selection using Cross-Validated SuperLearner

Super LeArner Predictions using NAb Panels