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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.
🍊 📊 💡 Orange: Interactive data analysis
Python code for common Machine Learning Algorithms
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
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 python library for decision tree visualization and model interpretation.
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 Best Artificial Intelligence Resources
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
A curated list of gradient boosting research papers with implementations.
Generalized Random Forests
An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)