An R package for selecting variables in regression models

Parkinson's Progression Marker Initiative data science challenge, 2016

Demo of 1-pass variable importance using t-digests

Demo notebook and data for Spark Summit Dublin 2017: One-Pass Data Science with Generative T-Digests

🎯🎓 An introductory workshop lecture on a generalized framework for Targeted Learning using the tmle3 R package

Simulating Supervised Learning Data

Important Variable selection

Automated Bidirectional Stepwise Selection On Python

R script to rank and select variables based on their importance/predictive power

A large dataset for studying the early readmission of diabetic patients problem

📦 🔬 R/methyvim: Targeted, Robust, and Model-free Differential Methylation Analysis

Reproduce analyses from "Efficient nonparametric statistical inference on population feature importance using Shapley values"

Variable importance via oscillations

We used different machine learning approaches to build models for detecting and visualizing important prognostic indicators of breast cancer survival rate. This repository contains R source codes for 5 steps which are, model evaluation, Random Forest further modelling, variable importance, decision tree and survival analysis. These can be a pipe…

Code for Variable Selection in Black Box Methods with RelATive cEntrality (RATE) Measures

Stability Selection with Error Control

Code for the paper 'Variable Selection with Copula Entropy' published on Chinese Journal of Applied Probability and Statistics

Perform inference on algorithm-agnostic variable importance in Python

SVC and KNN methods were used to predict whether mushrooms are poisonous or edible according to their properties. Random forest and chi-square variable selection methods were applied and the 10-fold cross validation method was used and f1 scores were calculated by re-estimating. Finally, the models were compared.

🎯 💯 Targeted Learning and Variable Importance for the Causal Effect of an Optimal Individualized Treatment Intervention