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
A collection of additional screening algorithms for SuperLearner
Implementing Gradient Boosting , XGboost, AdaBoost & SuperLearner in Python and comparing their classification accuracy of the two methods.
Introduction to Double Robust Estimation for Causal Inference
SuperLearner R package: prediction model ensembling method
A parallel implementation of the Super Learner estimator in Python. Winner of the Statistical Learning course contest!
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
Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.
npRR: Model-robust inference for the conditional relative risk function using targeted machine learning
Hack Aotearoa 2020
Ensembled Feature Selection using Cross-Validated SuperLearner
SuperLearner Tutorial for Epidemiologists & Biostatisticians — hands-on R tutorial with clinical application, cross-validated ensemble ML, and TMLE bridge.
Super LeArner Predictions using NAb Panels
Implementation of the Poisson Superlearner and piece-wise constant hazard models for risk predictions.
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