CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.
[NeurIPS 2022] (De-)Randomized Smoothing for Decision Stump Ensembles
Comprehensive benchmark study of feature selection techniques for predictive machine learning models on tabular data. Various feature selection methods are evaluated across different data characteristics and predictive scenarios.
This project uses EEG data to detect schizophrenia, achieving a robust classifier with LGBM, boasting a ROC AUC of 95.96% and an accuracy of 90%
👨💻 This repository shows how machine learning and SHAP can be leveraged to understand the reasons of production downtime ⌛
Detects anomalies using the Isolation Forest algorithm, with clear visual comparison between original data and anomaly-marked data in an unsupervised learning setup.
German Credit Data - 1994
Machine learning pipeline for multi-class treatment prediction in lung adenocarcinoma (LUAD) using patient-level molecular profiles, featuring ensemble-based model aggregation, benchmarking across diverse classifier architectures, and systematic performance evaluation.
I and my team participated in the Amazon ML Challenge, a national-level machine learning competition where we tackled real-world data problems and built predictive models using advanced ML techniques.
For this project, we will analyze publicly available data from LendingClub.com, which connects borrowers needing money with investors. The goal is to create a model that predicts the likelihood of borrowers repaying their loans. We will focus on Lending Club's data from 2007-2010 to classify and determine the repayment behavior pre-2016.
Extreme gradient boosting library ported from XGBoost with subsampling, missing value routing, gamma pruning, and full model serialization.
Tabular classification project with Machine Learning models
Data Science portfolio
Machine Learning Project at Kampus Merdeka Program
End-to-end house price prediction pipeline using the Ames dataset. Covers EDA, preprocessing, 5-fold cross-validation, and hyperparameter tuning across 9 models, including regularized linear models, boosting ensembles, and an MLP. Includes SHAP-based interpretability and MLflow experiment tracking, with best models achieving R² ≈ 0.90–0.94.
A nerdo practices logic living behind ML packages over a notebook dump
Predicting secondary school dropout rates across Colombian municipalities (2011–2023) with Decision Tree, Random Forest and Gradient Boosting regressors, following the CRISP-DM methodology.
Data-variance capture ability of Composition-descriptors while predicting the band gap (primarily semiconductor family choosen)
To associate your repository with the tree-based-models topic, visit your repo's landing page and select "manage topics."