There are 1 repository under bagging-ensemble topic.
Top Machine Learning Algorithms Detailed in Python and Preprocessing for Machine Learning
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
This is a friend recommendation systems which are used on social media platforms (e.g. Facebook, Instagram, Twitter) to suggest friends/new connections based on common interests, workplace, common friends etc. using Graph Mining techniques. Here, we are given a social graph, i.e. a graph structure where nodes are individuals on social media platforms and a directed edges (or 'links') indicates that one person 'follows' the other, or are 'friends' on social media. Now, the task is to predict newer edges to be offered as 'friend suggestions'.
https://teacher.bupt.edu.cn/zhuchuang/en/index.htm
The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidates more likely to have the visa certified.
Welcome to the Machine Learning Repository - Part 4! This repository focuses on unsupervised machine learning algorithms, particularly clustering techniques, and explores the fascinating world of ensemble methods, including boosting and bagging.
Understand and code some basic algorithms in machine learning from scratch
Application of various text classification algorithms on multiple datasets.
Repository of explaination and python codes with Scikit-Learn for different ML algorithms.
Machine Learning Application In The Medical Field Used for predicting the occurrence of stroke for the patients depends on the patient information
The project includes building seven different machine learning classifiers (including Linear Regression, Decision Tree, Bagging, Random Forest, Gradient Boost, AdaBoost, and XGBoost) using Original, OverSampled, and Undersampled data of ReneWind case study, tuning hyperparameters of the models, performance comparisons, and pipeline development for productionizing the final model.
Banking-Dataset-Marketing-Targets
Use Random Forest to prepare a model on fraud data treating those who have taxable income <= 30000 as "Risky" and others are "Good"
machine learning ensemble learning types in easy steps with examples
Projects on Classification and Regression
ML models for HR classification problem. For more information please visit the link: https://datahack.analyticsvidhya.com/contest/wns-analytics-hackathon-2018-1/
Machine Learning algorithms from-scratch implementation. It covers most Supervised and Unsupervised algorithms. Homework assignments and Projects for graduate level Machine Learning Course taught by Dr Manfred Huber at UTA during Spring 21
A ML application(deployed on flask) to detect heart disease in patients based on medical features.
Developed and evaluated machine learning and deep learning models for detecting financial fraud.
Developed a ML assisted stock trading strategy to long or short a stock by training a random forest learner (random tree with bagging), details see the Final-Project-Report.
Predicting developer's salary from Stack Overflow Annual Developer Survey (https://insights.stackoverflow.com/survey)
Brief theoretical description about Random forest and application about the same.
Decision trees are supervised learning models used for problems involving classification and regression.
Stepwise Multiple Linear Regression (With Interactions) and Random Forest Regression on predicting the Productivity of the Garment Factory Workers
A two-tier convolution neural network hybrid model for malignant melanoma prediction.
ML project based on intrusion detection system trained dataset
Templated boilerplate for experiments in Active and Ensemble Learning.
learning python day 14
Code of the Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN on ICEEM 2023
Predicting Colorado forest cover types using diverse ML models for classification. Baseline creation, feature selection, comparison, and tuning optimize accuracy in this University of Ottawa Master's Machine Learning course final project (2023).
Ensembles of machine learning models
Classification problem using multiple ML Algorithms
Bagging is the term from "Bootstrap Aggregation Algorithm", That is for Low Bias & Low Variance