extra-trees

using Drebin dataset to distinguish between malwares and not malwares

Rice Crop Yield Estimation Using Satellite Data - EY Open Science Data Challenge 2023

Traductor del alfabeto estático de lengua de señas peruana para web y Android, con MediaPipe y Extra Trees.

End-to-End Used Car Price Prediction using Ensemble Learning | Extra Trees, Random Forest, Gradient Boosting | Python • Scikit-learn

This repository contains the source code to reproduce the paper "Feature-based No-Reference Video Quality Assessment using Extra Trees".

Fast gradient-boosted decision trees for Apple Silicon. A lightweight, sklearn-style Python API powered by MPS and Metal.

Project 2 Group C - Predicting FinTech Bootcamp Graduate Salaries

Machine learning pipeline for predicting employee attrition using ensemble models and feature engineering.

Prediction of forest cover type in Python.

Predicting Appliance Energy use in Residential Buildings.

🏆 Chemical Process Yield Prediction for a competitive ML hackathon using feature engineering and Extra Trees regression.

Mitsui Kaggle — ExtraTrees with single-lag & group-lag features

Data Science Project (Autonomous Driving Dataset Analysis)

End-to-end Data Science project on Amazon Sales. Features data cleaning, EDA, outlier detection, and predictive modeling using Python, Pandas, and Scikit-learn.

An AI-driven hotel classification system using Extra Trees with SHAP-based explainability. Includes Streamlit frontend and FastAPI backend for deployment.

Precision-preserving optimized inference for one-dimensional reactive contaminant transport, with validated ADR1D-ML-Compact and ADR1D-NN-Fast models and reproducible Python workflows.

Sampling Assignment: Download dataset, balance classes, apply ML models with different sampling techniques to evaluate performance.

LaTeX source for the article 'Improving Bimonthly Landscape Monitoring in Morocco, North Africa, by Integrating Machine Learning with GRASS GIS' (Lemenkova, Geomatics 2025, 5(1):5). Decision Tree and Extra Trees classification of a bimonthly Landsat time series in GRASS GIS.

An explainable machine learning project for heart disease prediction, featuring exploratory data analysis, model comparison, hyperparameter tuning, and SHAP explanations.

Classical machine learning techniques to the problem of protein function prediction based solely on amino acid sequences. The task is formulated as a supervised multi-class classification problem, using functional annotations derived from the UniProt and Gene Ontology databases.