importance

Source code "Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem." @ CVPR2020

This project demonstrates the use of generic bi-directional LSTM models for predicting importance of words in a spoken dialgoue for understanding its meaning. The model operates on human-annotated corpus of word importance for its training and evaluation. The corpus can be downloaded from: http://latlab.ist.rit.edu/lrec2018

Workflow to generate interactive feature selection report for cross-sectional or longitudinal studies

Model-agnostic feature importance with python.

This repository contains the implementation of ITErpretability, a new framework to benchmark treatment effect deep neural network estimators with interpretability. For more details, please read our NeurIPS 2022 paper: 'Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability'.

storage for thoughts, notes, etc.

Thesis titled "Geospatial Semantic Pattern Recognition in Volunteered Geographic Data Using the Random forest Algorithm" for the degree of Masters of Spatial Analysis at Ryerson University in 2016

Code for Master Thesis

Nonparametric variable importance assessment

Scraping Rotten Tomatoes to Uncover Hidden Trends

An implementation of a decision tree classifier to predict the most present age in each urban area in the data set.

Python module to calculate local variable importance with the global model

Companion code for walkthrough: http://amunategui.github.io/variable-importance-shuffler/

A word cloud is a graphical representation of the words in a text in which the size or color of each word indicates the frequency or importance of that word in the text. Here, it is created by referring to information contained in a file such as an Excel file.

CNNAMON: a convolutiIonal neural network interpretability framework for deciphering regulatory motif insights

Variable importance through causal inference, with Alan Hubbard