boosting-algorithms

Text Classification Algorithms: A Survey

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

Machine Learning for High Energy Physics.

Boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data. The current relase version can be found on CRAN (http://cran.r-project.org/package=mboost).

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

The Tidymodels Extension for Time Series Boosting Models

Deep Boosting for Image Denoising in ECCV 2018 and its Real-world Extension in IEEE Transactions on Pattern Analysis and Machine Intelligence

collections of data science, machine learning and data visualization projects with pandas, sklearn, matplotlib, tensorflow2, Keras, various ML algorithms like random forest classifier, boosting, etc

In depth machine learning resources

Programmable Decision Tree Framework

Run XGBoost model and make predictions in Node.js

Catboost Go Wrapper

Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).

Deepboost R-package for submission

Boosting Functional Regression Models. The current release version can be found on CRAN (http://cran.r-project.org/package=FDboost).

Play around with NGBoost and compare with LightGBM and XGBoost

MILBoost and other boosting algorithms, compatible with scikit-learn

{PySpark, R, Python}: Several Data Science projects

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