Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现

A collection of research papers on decision, classification and regression trees with implementations.

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'

ML-Ensemble – high performance ensemble learning

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

Python package for stacking (machine learning technique)

(AAAI' 20) A Python Toolbox for Machine Learning Model Combination

RMDL: Random Multimodel Deep Learning for Classification

A Python library for dynamic classifier and ensemble selection

Ensemble learning related books, papers, videos, and toolboxes

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

Scikit-learn compatible estimation of general graphical models

Stacked Generalization (Ensemble Learning)

The 2nd place Solution to the Youtube-8M Video Understanding Challenge by Team Monkeytyping (based on tensorflow)

DeepSuperLearner - Python implementation of the deep ensemble algorithm

ICDE'20 | A general & effective ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架

Unofficial Pytorch code for "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models"

A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

Fully automatic brain tumour segmentation using Deep 3-D convolutional neural networks

python library implementing ensemble methods for regression, classification and visualisation tools including Voronoi tesselations.

A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

Predicting Amsterdam house / real estate prices using Ordinary Least Squares-, XGBoost-, KNN-, Lasso-, Ridge-, Polynomial-, Random Forest-, and Neural Network MLP Regression (via scikit-learn)

An automatic ensembler of machine learning models in python

Supervised Classification of bird species 🐦 in high resolution images, especially for, Himalayan birds, having diverse species with fairly low amount of labelled data

Top 1% rankings (22/3270) code sharing for Kaggle competition Sberbank Russian Housing Market: https://www.kaggle.com/c/sberbank-russian-housing-market

Arabic NER system with a strong performance

Combine models, easily. 🚀