ck37

atlantic-causal-2017

Targeted Learning entry in the Atlantic Causal Inference Conference's 2017 competition

ck37r

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

coral-ordinal

Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)

Predictive-Modeling-in-R

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

superlearner-guide

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

varimpact

Variable importance through targeted causal inference, with Alan Hubbard

Machine-Learning-in-R

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

R-Deep-Learning

Workshop (6 hours): Deep learning in R using Keras. Building & training deep nets, image classification, transfer learning, text analysis, visualization

Unsupervised-Learning-in-R

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

SuperLearner

Current version of the SuperLearner R package

ck37/coral-ordinal

Tensorflow Keras implementation of ordinal regression using consistent rank logits (CORAL) by Cao et al. (2019)

ck37/varimpact

Variable importance through targeted causal inference, with Alan Hubbard

ck37/superlearner-guide

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

ck37/ck37r

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

ck37/Predictive-Modeling-in-R

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

ck37/hpc-savio-xsede

Multicore and multi-node parallel R computation via SLURM on the Savio cluster at UC Berkeley, plus XSEDE

ck37/featurerank

Ensemble feature ranking for SuperLearner variable selection

ck37/clinsent

Estimate sentiment in clinical notes via keywords or deep learning models

ck37/mimic-clinical-sentiment

Manuscript under review

ck37/tlmixture

Data-adaptive creation of exposure (treatment) mixtures using targeted learning

ck37/toxic-comments-fastai-pytorch

Analyzing Jigsaw's toxic comments Kaggle challenge using fastai + pytorch

ck37/Machine-Learning-in-R

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

ck37/SuperLearner

SuperLearner R package: prediction model ensembling method

ck37/causalquantile

R package for estimating quantile treatment effects

ck37/chest-pain-risk-prediction

Article under review

ck37/ck37

ck37/darwin-py

Library and commandline tool for managing datasets on darwin.v7labs.com

ck37/dcurves

Decision Curve Analysis

ck37/embarcadero

🌲🌉 BART species distribution models 🌉 🌲

ck37/fastglm

Fast glm fitting via RcppEigen

ck37/iml

iml: interpretable machine learning R package

ck37/mlr3book

Introduction to mlr3

ck37/mlr3extralearners

Extra learners for use in mlr3.

ck37/mlr3viz

Visualizations for mlr3

ck37/Phase2.1DataRPackage

ck37/Phase2.1DockerAnalysis

ck37/Phase2.1SeverityAugRPackage

ck37/quicksectx

ck37/sl3

💪🤓 Modern Super Learning using Pipelines

ck37/tuneRanger

Automatic tuning of random forests