Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
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Updated
Jul 30, 2024 - R
Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles
Financial distress prediction from Kaggle
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.
Github Repository for the paper "Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study" - BIBM 2023
Repo for Manzano Analytics HTML website
ML-solution of the case of the District hackathon Leaders of Digital 2023. The task was to predict accidents (accidents, pipe ruptures, fires) based on the weather forecast for each of the urban districts. Gradient boosting (macro f1), cross-validation, shap values.
Loan-Default-Prediction
This project was developed during the course Laboratory of Computational Physics
In this project, we have to create a predictive model which allows the company to maximize the profit of the next marketing campaign
Predicción del precio de venta de las viviendas en venta y de las viviendas en alquiler de Barcelona.
Explainable Landscape-Aware Optimization Performance Prediction
Experimenting with SHAP values to explain how a given Machine Learning model works.
Prediction if patients with symptoms have COVID-19 based on clinical variables (blood related variables, urine related variables, age, etc)
Android malware detection using machine learning.
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.
🐍 Mental Maps Related to Contents in Data Science 🐍
Generate predictive model using supervised learning method to enhanced coupon acceptance rate using python.
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 production. A Neural Network was trained using Tensorflow, which was later tuned in order to achieve high-accuracy quality predictions.
XAI analytics to understand the working of SHAP values
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