tree-based-models

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%

German Credit Data - 1994

👨‍💻 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.

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.

Tabular classification project with Machine Learning models

Data Science portfolio

Machine Learning Project at Kampus Merdeka Program

A nerdo practices logic living behind ML packages over a notebook dump

Data-variance capture ability of Composition-descriptors while predicting the band gap (primarily semiconductor family choosen)

Usually tree-based and neural network regressors work better for regression tasks than linear regression models, because they can capature complex or subtle non-linear patterns in data.

Tree-based models are appealing for price modeling due to their high performance but they can be unstable. Due to competition between insurers, unstable models increase the risk that the overall premium is too small to cover the losses. The thesis propose various strategies for improving the stability of tree-based models.

Automated reasoning 🤖 for CoT prompting 💬 using explainability attributes from tree-based 🌳 models for binary classification on tabular datasets