Ensembled Feature Selection using Cross-Validated SuperLearner
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Updated
Nov 6, 2023 - R
Ensembled Feature Selection using Cross-Validated SuperLearner
A collection of additional screening algorithms for SuperLearner
A parallel implementation of the Super Learner estimator in Python. Winner of the Statistical Learning course contest!
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.
npRR: Model-robust inference for the conditional relative risk function using targeted machine learning
Ensemble feature ranking for SuperLearner variable selection
Implementing Gradient Boosting & SuperLearner in R and compare the classification accuracy of the two methods.
Super LeArner Predictions using NAb Panels
Workshop (6 hours): preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, superlearner ensembles
Workshop (2-6 hours): cleaning, missing value imputation, EDA, ensemble learning, calibration, variable importance ranking, accumulated local effect plots. WIP.
Hack Aotearoa 2020
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
SuperLearner guide: fitting models, ensembling, prediction, hyperparameters, parallelization, timing, feature selection, etc.
Implementation of Super Learner classifier and comparison with Logistic regression, SVC and Random Forests classifier.
Introduction to Double Robust Estimation for Causal Inference
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