Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
-
Updated
Aug 26, 2024 - Jupyter Notebook
Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
This project aims to study the influence factors of international students' mobility with the case of international students from B&R countries studying in China.
Meta-analysis of learning and memory in PTSD
Variable Importance Plots (VIPs)
Partial dependence plot tool
Trained a classifier by using labeled data and oversampling and undersampling techniques to predict if a borrower will default on a loan. The model is intended to be used as a reference tool to help investors make informed decisions about lending to potential borrowers based on their ability to repay. The purpose is to lower risk & maximize profit.
Complex odor analysis and interpretation
The goal of SHAP is to explain the prediction of an instance x by computing the contribution of each feature to the prediction. The SHAP explanation method computes Shapley values from coalitional game theory. The feature values of a data instance act as players in a coalition.
Individual Conditional Expectation (ICE) plots display one line per instance that shows how the instance's prediction changes when a feature changes. The Partial Dependence Plot (PDP) for the average effect of a feature is a global method because it does not focus on specific instances, but on an overall average.
In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.
A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.
This project contains the data, code and results used in the paper title "On the relationship of novelty and value in digitalization patents: A machine learning approach".
Python package to visualize and cluster partial dependence.
Data Mining Final Project
Predicting product recommendation score using the data available on the website of the client
Contains analysis of Lyft ride attributes and how it affects demand surge in the city of Boston.
Meta-analysis of the rodent object-in-context task
Kaggle kernels and the respective implementations of ML procedures.
Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18
Predict churning or not from the real-world data of a ridesharing app
Add a description, image, and links to the partial-dependence-plot topic page so that developers can more easily learn about it.
To associate your repository with the partial-dependence-plot topic, visit your repo's landing page and select "manage topics."