Comprehensive dimensionality reduction and cluster analysis toolset

Tools to Support Relative Importance Analysis

Creating predictive models to classify Trump's vote share and clustering counties based on demographics and economic variables. Report findings in PDF with detailed methodologies, model assessments, and R code for the project.

Explainable Machine Learning in Survival Analysis

🌳 🎯 Cross Validated Decision Trees with Targeted Maximum Likelihood Estimation

Filter-based feature selection for mlr3

This project relates to a methodological report on canton Bern forest fire modelling and mitigation strategies.

2022년 2학기 팀 프로젝트 : 도금공정 데이터 셋 분석(k-인공지능 제조데이터 분석 경진대회)

Fast approximate Shapley values in R

SurvSHAP(t): Time-dependent explanations of machine learning survival models

Perform inference on algorithm-agnostic variable importance

💫 🎯 Automatic identification of variable and interaction importance using basis functions and non-parametric estimation of interactions/effect modification using joint stochastic interventions.

Variable Importance Plots (VIPs)

Code and tutorials for implementing the GlObal And Local Score

📦 🎲 R/txshift: Efficient Estimation of the Causal Effects of Stochastic Interventions, with Corrections for Outcome-Dependent Sampling

🎯🎓 Generalized Targeted Learning Framework

💬 Talk on causal inference and variable importance with stochastic interventions under two-phase sampling

Prediction of equipment failures

In this project, I use 3 machine learning models (CART, Random Forest and ANN) to predict the claim frequency for a travel insurance firm. I also evaluate which of the three models is most suitable for our dataset.

Build a predictive model for the sale prices of homes in a city and explore potential equity issues with the real-estate assessment process