There are 22 repositories under random-forest topic.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Python code for common Machine Learning Algorithms
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
A collection of research papers on decision, classification and regression trees with implementations.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Text Classification Algorithms: A Survey
A curated list of data mining papers about fraud detection.
利用pytorch实现图像分类的一个完整的代码,训练,预测,TTA,模型融合,模型部署,cnn提取特征,svm或者随机森林等进行分类,模型蒸馏,一个完整的代码
This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
A curated list of gradient boosting research papers with implementations.
An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)
gesture recognition toolkit
A curated list of Best Artificial Intelligence Resources
ThunderGBM: Fast GBDTs and Random Forests on GPUs
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
Machine Learning library for the web and Node.
Fast SHAP value computation for interpreting tree-based models
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
Machine learning for C# .Net
🔥🌟《Machine Learning 格物志》: ML + DL + RL basic codes and notes by sklearn, PyTorch, TensorFlow, Keras & the most important, from scratch!💪 This repository is ALL You Need!
Machine Learning Lectures at the European Space Agency (ESA) in 2018
Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
A python library to build Model Trees with Linear Models at the leaves.