A Multi-Output Regression Framework in Python

Deep Learning for Multi-Output Regression using Gradient Boosting

Gradient Boosted Neural Network - Multi Output

Condensed-Gradient Boosting

In This Notebook I've Build a Machine Learning Model to predict the relation between users and restaurants, and this model is piece of my graduation project.

Multi-Output Infinite Horizon Gaussian Process

[EUVIP 2021] The official repo for Visual Quality and Security Assessment of Perceptually Encrypted Images based on Multi-Output Deep Neural Network (VSMML)

Automated IELTS scoring pipeline using a Multi-Output Random Forest regressor and TF-IDF vectorization to predict 4 rubric categories simultaneously.

자율주행 센서의 안테나 성능 예측 AI 경진대회, LG AI Research (2022.08.01 ~ 2022.08.26)

Stock Returns Prediction

Ridge Regression and Random Forest Regression models are build predictive models on the estimation of energy performance of residential buildings.

Multi-Output Regression for Integrated Prediction of Valence and Arousal in EEG-Based Emotion Recognition

Machine Learning model for predicting multi-stage continuous-flow manufacturing process outputs using industrial sensor data and Multi-Output Random Forest Regression.

A personalized finance management tool, use to provide recommendations based on the interests and expenses of the user. Helps in keeping the record of the incomes and expenditures made throughout the year/month.

This repository contains the resources our team used through the course of the CLEF competition.

A multi-target regression algorithm based on Gaussian process regression

Multi-output regression problem done for the prediction of truck axle weights.

Designed as a training document explaining how to apply the multi-output method in time series forecasting. This means a single model can simultaneously predict multiple future values. more stable results than the recursive method.