21 citations · 108 across the 18 of their papers we have counts for
3 papers · 1 filter
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons
Linwei Hu, Vijayan N. Nair, Agus Sudjianto +2
In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best predictive performance. To understand and explain the model results, o…
Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularization
Shijie Cui, Agus Sudjianto, Aijun Zhang +1
Gradient-boosted decision trees (GBDT) are widely used and highly effective machine learning approach for tabular data modeling. However, their complex structure may lead to low ro…
PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics
Agus Sudjianto, Aijun Zhang, Zebin Yang +2
PiML (read -ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with mac…