boosting

There are 366 repositories under boosting topic.

  • SMARTboost.jl

    SMARTboost (boosting of smooth symmetric regression trees)

    Language:Julia13
  • logitboost

    LogitBoost classification algorithm built on top of scikit-learn

    Language:Python13
  • Boosting-multitask-learning-on-graphs

    A boosting procedure for multitask learning on graph-structured data

    Language:Python12
  • steam-hour-booster

    steam-hour-booster

    Farm your in-game hours on Steam

    Language:TypeScript12
  • ecgn

    Concepts used: kNN, SVM, boosting (XGBoost, Gradient boosting, Light GBM, AdaBoost, Random Forests), deep learning (CNN, LSTM), ensembles (model stacking), transfer learning.

  • song-popularity-prediction

    Song Popularity Prediction Using Machine Learning Algorithms

    Language:Jupyter Notebook12
  • BooVAE

    Code repository of the paper "BooVAE: Boosting Approach for Continual Learning of VAE" published at NeurIPS 2021. https://arxiv.org/abs/1908.11853

    Language:Python12
  • Dada

    source code of AISTATS 2020 paper: Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs

    Language:Jupyter Notebook12
  • EvoLinear.jl

    Linear models

    Language:Julia10
  • Spam-Messages-Detection

    Project building ML & DL models to detect spam messages.

    Language:Jupyter Notebook9
  • simple-gradient-boosting

    Very simple and short implementation of gradient boosting in 18 lines of code

    Language:Jupyter Notebook9
  • Machine-Learning-Handwritte-Notes

    Entire Machine Learning Hand Written Notes

  • Machine-Learning

    Regression, Classification, Clustering, Dimension-reduction, Anomaly detection

    Language:Jupyter Notebook8
  • Machine-Learning-Codes

    Machine Learning is not a MAGIC but MATH

    Language:Jupyter Notebook8
  • Avito-Demand-Prediction-Challenge

    It is a Competition for Regression Challenge held by Kaggle, It is based on a Avito Dataset whose size is 123GB which can be accessed from Kaggle, I have done Data Pre-processing, feature engineering, feature extraction, data visualization, machine learning, stacking and boosting

    Language:Jupyter Notebook8
  • Titanic-Passenger-Survival-Prediction

    Using Classification Techniques, Data reprocessing, Feature Engineering, Feature Extraction and Classification Algorithms from Machine Learning to Predict who can Survive the attack of Tsunami.

    Language:Jupyter Notebook8
  • Kaggle-House-Prices-Advanced-Regression-Techniques

    House Prices: Advanced Regression Techniques - Kaggle competition

    Language:Jupyter Notebook8
  • mlflow-demo

    Simple Demo of MLflow Project

    Language:Jupyter Notebook8
  • XLabel

    XLabel: An Explainable Data Labeling Assistant

    Language:Jupyter Notebook7
  • ATPTennisMatchPredictions

    Tennis Match Predictions using Machine Learning

    Language:Jupyter Notebook7
  • stackboost

    Open source gradient boosting library

    Language:Python7
  • Predicting_Money_Spent_at_Resort

    It is From Analytics Vidhya Hackathons, Sponsored by Club Mahindra. It is based on Regression Problem, Where Accuracy matters the most, It is measured by RMSE Score. Different Techniques such as Stacking, Ensembling, Boosting and Scientific Operations such box-cox Operations to reduce skewness of the data.

    Language:Jupyter Notebook7
  • AdaBoost

    A simplified implement of Adaptive boosting.

    Language:Python7
  • GreedyBoost

    Implementation of Modified Online Adaboost-ing algorithm

    Language:Python7
  • Discord-Boost-Bot

    a discord bot which boost ur server on command

    Language:Go6
  • Machine-Failure_Prediction_EnsembleMethods_ModelTuning

    This project predicts wind turbine failure using numerous sensor data by applying classification based ML models that improves prediction by tuning model hyperparameters and addressing class imbalance through over and under sampling data. Final model is productionized using a data pipeline

    Language:Jupyter Notebook6
  • Analysis-and-prediction-of-online-shoppers-purchasing-intention-using-various-algorithms-CAPSTONE

    Build a predictive machine learning model that could categorize users as either, revenue generating, and non-revenue generating based on their behavior while navigating a website. In order to predict the purchasing intention of the visitor, aggregated page view data kept track during the visit along with some session is used and user information as input to machine learning algorithms. Oversampling/Undersampling and feature selection techniques are applied to improve the success rates and scalability of the models.

    Language:Jupyter Notebook6
  • Machine_Learning_Hackathons

    Machine learning and Deep Learning Hackathon Solutions

    Language:Jupyter Notebook6
  • Credit-Risk-Analysis

    Credit-Risk-Analysis

    Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods

    Language:Jupyter Notebook6
  • house_prices_melbourne

    A compilation of different models that predict a home's value (in Melbourne, Australia) and determine which model performs better and why.

    Language:Jupyter Notebook6
  • Societe-General

    Solution for ENS - Societe Generale Challenge (1st place).

    Language:R6
  • snapboost

    Heterogeneous Newton Boosting Machine using decision trees and kernel ridges as learners.

    Language:Python5
  • Tutorial-Machine-Learning-Arboles

    Los árboles de decisión son uno de los algoritmos clásicos de machine learning ya que nos ayudan a visualizar las predicciones hechas por nuestro modelo. En este tutorial vemos su uso para regresiones lineares y clasificación, así como herramientas de ensamble como bagging y boosting.

    Language:Jupyter Notebook5
  • Cascade_Toolbox

    A toolbox to simplify training, testing, and running HAAR/LBP cascades for object detection

    Language:C++5
  • ml-earthquake

    machine learning semester project to predict earthquake damage levels

    Language:Jupyter Notebook5
  • weightedFastText

    Weighted FastText is a library for fast text classification with weighted examples

    Language:C++5