Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
翻译 - 快速构建可解释的 AI 仪表板，显示所谓的“黑盒”机器学习模型的内部工作原理。
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 mod...
A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc
#计算机科学#Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
#计算机科学#In this project, we have to create a predictive model which allows the company to maximize the profit of the next marketing campaign
#计算机科学#🐍 Mental Maps Related to Contents in Data Science 🐍
Github Repository for the paper "Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study" - BIBM 2023
This project uses Explainable AI (XAI) to interpret machine learning models for diagnosing faults in industrial bearings. By applying SVM and kNN models and leveraging SHAP values, it enhances the tra...
#计算机科学#Android malware detection using machine learning.
#计算机科学#The purpose of this work is the modeling of the wine preferences by physicochemical properties. Such model is useful to support the oenologist wine tasting evaluations, improve and speed-up the wine p...
Prediction if patients with symptoms have COVID-19 based on clinical variables (blood related variables, urine related variables, age, etc)
WiDS Datathon 2020 on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.
Financial distress prediction from Kaggle
#计算机科学#Generate predictive model using supervised learning method to enhanced coupon acceptance rate using python.
Jantahack : BigMart Sales Prediction using LGBM Regressor and Model interpretation using SHAP
Explainable Landscape-Aware Optimization Performance Prediction
This project was developed during the course Laboratory of Computational Physics
#计算机科学#Experimenting with SHAP values to explain how a given Machine Learning model works.
Repo for Manzano Analytics HTML website