Popular GitHub Repositories for Bagging

A unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.

In this project, I implemented several ensemble methods (including bagging, AdaBoost, SAMME, stacking, snapshot ensemble) for a normal CNN model and Residual Neural Network.

ML-algorithms from scratch using Python. Classic Machine Learning course.

Building Decision Trees From Scratch In Python

Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.

I will update this repository to learn Machine learning with python with statistics content and materials

Web application for engineering students to predict appropriate job roles using Machine learning and other guidance material like job descriptions, links to courses, etc.

This is a Statistical Learning application which will consist of various Machine Learning algorithms and their implementation in R done by me and their in depth interpretation.Documents and reports related to the below mentioned techniques can be found on my Rpubs profile.

Ensemble Learning for Apache Spark ?

This repository not only contains experience about parameter finetune, but also other in-practice experience such as model ensemble (boosting, bagging and stacking) in Kaggle or other competitions.

Supervised Machine Learning Analysis Using Classification Models

Implementation of bagging-based ensemble for solar irradiance prediction. Base learners used in ensemble learning is stacked-LSTM

Compare Naive Bayes, SVM, XGBoost, Bagging, AdaBoost, K-Nearest Neighbors, Random Forests for classification of Malaria Cells

Entire Machine Learning Hand Written Notes

Implementation of Google Quick Draw doodle recognition game in PyTorch and comparing other classifiers and features.

Unsupervised ensemble learning methods for time series forecasting. Bootstrap aggregating (bagging) for double-seasonal time series forecasting and its ensembles.

FastML Framework is a python library that allows to build effective Machine Learning solutions using luigi pipelines.

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.

Data Mining and Machine Learning APS Failure at Scania Trucks Data Set.

Basic Machine Learning Algorithms tutorials (Linear Regression, Logistic Regression, SVM, Random Forest, Bagging, KNN, K-Means ...)