casualitySensitivityLab

Quark Agent - Your AI-powered Android APK Analyst

Leave One Feature Out Importance

Features selector based on the self selected-algorithm, loss function and validation method

Routines and data structures for using isarn-sketches idiomatically in Apache Spark

Official repository of the paper "Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature Importance", M. Carletti, M. Terzi, G. A. Susto.

Using / reproducing DAC from the paper "Disentangled Attribution Curves for Interpreting Random Forests and Boosted Trees"

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

Demonstrates how to utilize XGBoost for traffic forecasting using data gathered from IoT sensors, highlighting its efficiency in processing complex datasets and delivering accurate predictions.

Customer Churn Analysis in R: Logistic, Classification Tree, XGBoost, Random Forest.

This repository represents several projects completed in IE HST's MS in Business Analytics and Big Data's Financial Analytics course.