bagging

vecxoz/vecstack

Python package for stacking (machine learning technique)

a-martyn/ISL-python

Solutions to labs and excercises from An Introduction to Statistical Learning, as Jupyter Notebooks.

mlr-org/mlr3pipelines

Dataflow Programming for Machine Learning in R

damianhorna/multi-imbalance

Python package for tackling multi-class imbalance problems. http://www.cs.put.poznan.pl/mlango/publications/multiimbalance/

serengil/decision-trees-for-ml

Building Decision Trees From Scratch In Python

sharmaroshan/Drugs-Recommendation-using-Reviews

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

nursnaaz/25DaysInMachineLearning

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

SudhakarKuma/Machine_Learning

A repository of resources for understanding the concepts of machine learning/deep learning. 

AaronWard/PU-learning-example

An example repo for how PU Bagging and TSA works.

forestry-labs/Rforestry

anishsingh20/Statistical-Learning-using-R

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.

sayaliwangane/INTELLIGENT-CAREER-GUIDANCE-SYSTEM

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

pierrenodet/spark-ensemble

Ensemble Learning for Apache Spark 🌲

frankkramer-lab/ensmic

An analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks

ysh329/awesome-deep-learning-finetune-experience

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.

wslc1314/TextSentimentClassification

TextSentimentClassification, using tensorflow.

rafaelvareto/HPLS-HFCN-openset

Paper: Towards Open-Set Face Recognition using Hashing Functions (IJCB'17)

zpysky1125/Ensembling

Sklearn implement of multiple ensemble learning methods, including bagging, adaboost, iterative bagging and multiboosting

LaxmiChaudhary/Modeling-of-strength-of-high-performance-concrete-using-Machine-Learning

sun1638650145/classicML

简单易用的经典机器学习框架

vikasgupta1812/Machine_Learning

Predictive Machine Learning projects

UnixJunkie/orf

OCaml Random Forests

anubhavshrimal/Quick-Draw

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

gulabpatel/Machine-Learning

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

Gurudev333/Loan_Prediction_by_ensemble_machine_learning_techniques

Used ensemble methods such as boosting, voting, Bagging

sahirnoorali/poker-hand-analysis

Analysis and Prediction of Poker Hand Strength

sharmaroshan/Don-t-Overfit

It is from Kaggle Competitions where the training dataset is very small and the testing dataset is very large and we have to avoid or reduce overfiting by looking for best possible ways to overcome the most popular problem faced in field of predictive analytics.

arunvignesh15/Machine-Learning

Learn and Explore

Aviator10/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.

codekhal/Inshorts-NLP

Analysed syntax and Semantics of Corpus of Text Documents Retrieved from Web Scraping of News articles from Inshorts and followed the Standard NLP Workflow of the CRISP-DM model.

ravitejaCR7/Machine-Learning

sergii1989/FastMLFramework

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

ArslanKAS/Python-Chilla-2.0

All the content that I learned through two Courses. One is called "Python Chilla" and the second one is called "100 Days of Machine Learning"

okozelsk/EasyML

An easy-to-use independent machine learning library for .net. It offers MLP models (including deep RVFL aka ELM) for common ML tasks as well as Reservoir Computer for efficiently solving ML tasks on time series data.

thieu1995/mealpy-text-classification

Text classification with Machine Learning and Mealpy