Handwritten digit recognition with MNIST & Keras
Targeted Maximum Likelihood Estimation for Hierarchical Data
Code for "Adaptive Selection of the Optimal Strategy to Improve Precision and Power in Randomized Trials"
A super learner was built by stacking logistic regression, random forest, and gradient boosting models (XGBoost) to predict whether a patient in the cardiac wards needs to be transferred to ICU.
TMLE and one-step estimators built on Riesz representers, unifying efficient estimation across causal inference settings: treatment effects, longitudinal regimes, mediation, quantiles, and two-phase sampling, with super learner nuisance estimation
Sklearn based Super Learning Stacked model
Nonparametric estimators of mediation effects with multiple mediators
Classify handwritten digits with TensorFlow/Keras, comparing single-layer, ANN, and CNN models on the MNIST dataset to find the best approach.
To associate your repository with the super-learner topic, visit your repo's landing page and select "manage topics."